Prediction of next place visits on online social networks

ABSTRACT

In one embodiment, a method includes receiving, from a client system associated with a user of an online social network, data indicating that the user is located at a first geographic location at a first time; accessing a first embedding representing a first place-entity corresponding to the first geographic location; accessing multiple second embeddings representing multiple respective second place-entities each corresponding to a second geographic location; calculating, a similarity metric between the embedding representing the first place-entity and each of the embeddings representing the second place-entities; ranking each of the second place-entities based on their calculated similarity metrics; and sending, to the client system, information associated with one or more second geographic locations corresponding to one or more second place-entities having a ranking greater than a threshold ranking.

TECHNICAL FIELD

This disclosure generally relates to online social networks, places, andgeo-location.

BACKGROUND

A social-networking system, which may include a social-networkingwebsite, may enable its users (such as persons or organizations) tointeract with it and with each other through it. The social-networkingsystem may, with input from a user, create and store in thesocial-networking system a user profile associated with the user. Theuser profile may include demographic information, communication-channelinformation, and information on personal interests of the user. Thesocial-networking system may also, with input from a user, create andstore a record of relationships of the user with other users of thesocial-networking system, as well as provide services (e.g., wall posts,photo-sharing, event organization, messaging, games, or advertisements)to facilitate social interaction between or among users.

The social-networking system may send over one or more networks contentor messages related to its services to a mobile or other computingdevice of a user. A user may also install software applications on amobile or other computing device of the user for accessing a userprofile of the user and other data within the social-networking system.The social-networking system may generate a personalized set of contentobjects to display to a user, such as a newsfeed of aggregated storiesof other users connected to the user.

A mobile computing device—such as a smartphone, tablet computer, orlaptop computer—may include functionality for determining its location,direction, or orientation, such as a GPS receiver, compass, gyroscope,or accelerometer. Such a device may also include functionality forwireless communication, such as BLUETOOTH communication, near-fieldcommunication (NFC), or infrared (IR) communication or communicationwith a wireless local area networks (WLANs) or cellular-telephonenetwork. Such a device may also include one or more cameras, scanners,touchscreens, microphones, or speakers. Mobile computing devices mayalso execute software applications, such as games, web browsers, orsocial-networking applications. With social-networking applications,users may connect, communicate, and share information with other usersin their social networks.

SUMMARY OF PARTICULAR EMBODIMENTS

In particular embodiments, the social-networking system 1160 may predicta second geographic location that a user will visit subsequent to theuser's presence at a first geographic location. In other words, thesocial-networking system 1160 may predict the next place the user willvisit given information about a previous place the user has visited orwill visit. In particular embodiments, a technical problem arising inthe field of geo-location may be providing users with informationassociated with a geographic location they will visit in the futurebased on a current geolocation. The technical solutions described hereinof automatically predicting a second geographic location that a userwill visit subsequent to the user's presence at a first geographiclocation and sending the user information associated with the secondgeographic location may improve computing processes related to receivingand executing queries by reducing the need for the user to send queriesrelated to the second geographic location, which may reduce thecomputing resources needed for such processes. In particularembodiments, the social-networking system 1160 may calculate theprobability P (k|X) that a user will visit a second geographic locationk subsequent to the user's presence at given a first geographic locationX. The social-networking system 1160 may use a model trained by machinelearning, which may take an input embedding representing a firstplace-entity corresponding to a first geographic location and output apredicted second place-entity corresponding to a second geographiclocation that the user will subsequently visit. In particularembodiments, the prediction may be a most probable second geographiclocation that the user will subsequently visit, a confidence scorerepresenting the confidence that the user will subsequently visit aparticular second geographic location, or it may be a ranked list ofprobable second geographic locations that the user will subsequentlyvisit. In particular embodiments, features of a place-entity may beselected by feature extraction to generate an embedding representing aplace-entity. The features extracted may include the category of theplace-entity (e.g., bar, gym), the time of the user's visit to thegeographic location corresponding to the place-entity (e.g., time ofday, day of week), the hours that the geographic location correspondingto the place-entity is open, popular hours of a geographic locationcorresponding to the place-entity, or any other suitable featuresrelated to the place-entity. User attributes may also be used togenerate an embedding (e.g., an embedding of a place-entity may bereconstructed from embeddings of the users visiting the geographiclocation corresponding to the place-entity). In particular embodiments,training data used to train a machine-learning model may be from usersof an online social network or any other suitable data. To train amachine-learning model, positive and negative pairs of first geographiclocations and second geographic locations may be used. In particularembodiments, a machine-learning model used to generate place-entityembeddings may be evaluated. As an example and not by way of limitation,for an evaluation set, the mean-reciprocal rank (MRR) may be used toevaluate the machine-learning model. In particular embodiments, amachine-learning model may be used to make a prediction about whichsecond geographic location a user will visit to subsequent to a givencurrent first geographic location of the user. In particularembodiments, a prediction may be used to suggest a second geographiclocation a user should visit next. In particular embodiments, aprediction may be used to deliver content associated with the predictedsecond geographic location to the client system 1130 of the user. Inparticular embodiments, a prediction may be used to predict a user'scurrent second geographic location based on a first geographic locationpreviously visited by the user. As an example and not by way oflimitation, a user's current location may be unknown or unable to beprecisely determined. The social-networking system 1160 may use aprevious first geographic location visited by the user to predict auser's current second geographic location. In particular embodiments,the prediction may be used as one of many factors in predicting theuser's current geographic location. As an example and not by way oflimitation, the predicted current second geographic location may bebased on the output of a machine-learning model as well as GPS data,wireless signal-information received by the client system 1130 of theuser, or any other suitable information. Although this disclosuredescribes predicting a second geographic location or place-entity basedon a first geographic location or first place-entity in a particularmanner, this disclosure contemplates predicting a second geographiclocation or place-entity based on a first geographic location or firstplace-entity in any suitable manner. Furthermore, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

In particular embodiments, the social-networking system 1160 maydetermine the most likely geographic location a user is located based onbackground signal-information from a first software application of aclient system 1130 of the user and a places-API call from a secondsoftware application of the client system 1130. In particularembodiments, a technical problem arising in the field of geo-locationmay be to disambiguate the geographic location of a user. In particularembodiments, using background signal-information, as described in thisdisclosure, may be used as an alternative to or in conjunction withother location data (e.g., GPS), which may provide the advantage of moreaccurately or precisely determining a geographic location of a user. Asan example and not by way of limitation, a user may be located at “TheOld Pro.” A first software application may collect and send to thesocial-networking system 1160 background signal-information associatedwith the geographic location (e.g., Wi-Fi signals at that location). Thesignal-information and a first client identifier of the client system1130 may be stored in a signal-information database. The first clientidentifier may be hashed. The user may check-in to “The Old Pro” using asecond software application. The second software application maycheck-in by using a places-API call of an online social network of thesocial-networking system 1160 to access information associated with aplace-entity corresponding to the geographic location “The Old Pro.” Theplace-entity and first client identifier may be recorded in an API-calllog. The first client identifier may be hashed. The social-networkingsystem 1160 may determine a correlation between the signal-informationand the place-entity corresponding to “The Old Pro” based on matchingthe hash of the first client identifier in the signal-information logand the hash of the first client identifier in the API-call log. Thesocial-networking system 1160 may update a place-entity database toindicate that the place-entity corresponding to “The Old Pro”corresponds to the background signal-information. A second client system1130 of a second user may send background signal-information. Thesocial-networking system 1160 may determine that the second clientsystem 1130 is located at “The Old Pro” based on determining that thebackground signal-information from the second client system 1130 matchesthe background signal-information from the first client system 1130.Although this disclosure describes determining a most likely geographiclocation of a user or a client system 1130 in a particular manner, thisdisclosure contemplates determining a most likely geographic location ofa user or a client system 1130 in any suitable manner. Furthermore,although this disclosure describes or illustrates particular embodimentsas providing particular advantages, particular embodiments may providenone, some, or all of these advantages.

In particular embodiments, the social-networking system 1160 mayidentify place-entities corresponding to invalid geographic locationsbased on information associated with place-entity nodes eachcorresponding to the geographic location. In particular embodiments,information associated with a place-entity in a place-entities graph maybe from a source such as user-created pages, pages from the internet,WIKIPEDIA, from users via the Open Graph protocol, or any other suitablesource. In particular embodiments, a technical problem arising in thefield of place-entities graphs may be providing users with informationassociated with valid place-entities. Because information aboutplace-entities may originate from many sources, some of which havehigher quality information than others, information about place-entitiesmay include low-quality or invalid information. In particularembodiments, low-quality or invalid place-entities may be automaticallydetected and filtered (e.g., place-entities corresponding to ageographic location that does not exist, duplicate place-entities,non-public or user-specific places, etc.). The technical solutionsdescribed herein of automatically detecting and filtering invalidplace-entities may improve computing processes related to storing andsearching for place-entities, which may reduce the computing resourcesneeded for such processes while still providing high quality informationto users in an automatic manner. Furthermore, the technical solutionsdescribed herein may reduce or eliminate the need to manually reviewplace-entities to determine whether the place-entities are valid and mayreduce the data throughput required to respond to a query by filteringout invalid place-entity results. In particular embodiments, aplace-entity cluster may comprise a plurality of place-entity nodescorresponding to a plurality of respective place-entities, eachplace-entity corresponding to the same geographic location. Inparticular embodiments, using information from multiple place-entitynodes in a place-entity cluster may provide information that indicateswhether the place-entity nodes correspond to an invalid geographiclocation. A cluster-quality score may be calculated for a place-entitycluster based on a plurality of embeddings representing a plurality ofrespective place-entities of the place-entity cluster. In particularembodiments, place-entity nodes of a place-entity cluster may be flaggedas being of high quality, and may be used in a “premium” or “verified”place-entity graph based on determining that the cluster-quality scoreis above a threshold score. In particular embodiments, place-entitynodes of a place-entity cluster associated with a cluster-quality scorebelow a threshold score may be reclassified as a non-place-entity node,suppressed or filtered during a search, or ranked lower in a search.Although this disclosure describes determining whether a place-entitycorresponds to a valid or invalid geographic location in a particularmanner, this disclosure contemplates determining whether a place-entitycorresponds to a valid or invalid geographic location in any suitablemanner. Furthermore, although this disclosure describes or illustratesparticular embodiments as providing particular advantages, particularembodiments may provide none, some, or all of these advantages.

The embodiments disclosed herein are only examples, and the scope ofthis disclosure is not limited to them. Particular embodiments mayinclude all, some, or none of the components, elements, features,functions, operations, or steps of the embodiments disclosed above.Embodiments according to the invention are in particular disclosed inthe attached claims directed to a method, a storage medium, a system anda computer program product, wherein any feature mentioned in one claimcategory, e.g. method, can be claimed in another claim category, e.g.system, as well. The dependencies or references back in the attachedclaims are chosen for formal reasons only. However any subject matterresulting from a deliberate reference back to any previous claims (inparticular multiple dependencies) can be claimed as well, so that anycombination of claims and the features thereof are disclosed and can beclaimed regardless of the dependencies chosen in the attached claims.The subject-matter which can be claimed comprises not only thecombinations of features as set out in the attached claims but also anyother combination of features in the claims, wherein each featurementioned in the claims can be combined with any other feature orcombination of other features in the claims. Furthermore, any of theembodiments and features described or depicted herein can be claimed ina separate claim and/or in any combination with any embodiment orfeature described or depicted herein or with any of the features of theattached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example artificial neural network.

FIG. 2 illustrates an example view of a vector space.

FIG. 3 illustrates example symbolic depictions of geographic locationson an example map.

FIG. 4 illustrates an example calculation of an example evaluationmetric.

FIG. 5 illustrates example features of example place-entities.

FIG. 6 illustrates an example method for predicting a second geographiclocation that a user will visit subsequent to the user's presence at afirst geographic location.

FIG. 7 illustrates an example map graphically representing examplegeographic locations.

FIG. 8 illustrates an example method for correlating signal-informationand place-entities.

FIG. 9 illustrates an example place-entity cluster.

FIG. 10 illustrates an example method 1000 for identifyingplace-entities corresponding to an invalid geographic location.

FIG. 11 illustrates an example network environment associated with asocial-networking system.

FIG. 12 illustrates an example social graph.

FIG. 13 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Artificial Neural Networks

FIG. 1 illustrates an example artificial neural network (“ANN”) 100. Inparticular embodiments, an ANN may refer to a computational modelcomprising one or more nodes. Example ANN 100 may comprise an inputlayer 110, hidden layers 120, 130, 140, and an output layer 150. Eachlayer of the ANN 100 may comprise one or more nodes, such as a node 105or a node 115. In particular embodiments, each node of an ANN may beconnected to another node of the ANN. As an example and not by way oflimitation, each node of the input layer 110 may be connected to one ofmore nodes of the hidden layer 120. In particular embodiments, one ormore nodes may be a bias node (e.g., a node in a layer that is notconnected to and does not receive input from any node in a previouslayer). In particular embodiments, each node in each layer may beconnected to one or more nodes of a previous or subsequent layer.Although FIG. 1 depicts a particular ANN with a particular number oflayers, a particular number of nodes, and particular connections betweennodes, this disclosure contemplates any suitable ANN with any suitablenumber of layers, any suitable number of nodes, and any suitableconnections between nodes. As an example and not by way of limitation,although FIG. 1 depicts a connection between each node of the inputlayer 110 and each node of the hidden layer 120, one or more nodes ofthe input layer 110 may not be connected to one or more nodes of thehidden layer 120.

In particular embodiments, an ANN may be a feedforward ANN (e.g., an ANNwith no cycles or loops where communication between nodes flows in onedirection beginning with the input layer and proceeding to successivelayers). As an example and not by way of limitation, the input to eachnode of the hidden layer 120 may comprise the output of one or morenodes of the input layer 110. As another example and not by way oflimitation, the input to each node of the output layer 150 may comprisethe output of one or more nodes of the hidden layer 140. In particularembodiments, an ANN may be a deep neural network (e.g., a neural networkcomprising at least two hidden layers). In particular embodiments, anANN may be a deep residual network. A deep residual network may be afeedforward ANN comprising hidden layers organized into residual blocks.The input into each residual block after the first residual block may bea function of the output of the previous residual block and the input ofthe previous residual block. As an example and not by way of limitation,the input into residual block N may be F(x)+x, where F(x) may be theoutput of residual block N−1, x may be the input into residual blockN−1. Although this disclosure describes a particular ANN, thisdisclosure contemplates any suitable ANN.

In particular embodiments, an activation function may correspond to eachnode of an ANN. An activation function of a node may define the outputof a node for a given input. In particular embodiments, an input to anode may comprise a set of inputs. As an example and not by way oflimitation, an activation function may be an identity function, a binarystep function, a logistic function, or any other suitable function. Asanother example and not by way of limitation, an activation function fora node k may be the sigmoid function

${{F_{k}( s_{k} )} = \frac{1}{1 + e^{- s_{k}}}},$the hyperbolic tangent function

${{F_{k}( s_{k} )} = \frac{e^{s_{k}} - e^{- s_{k}}}{e^{s_{k}} + e^{- s_{k}}}},$the rectifier F_(k)(s_(k))=max(0,s_(k)), or any other suitable functionF_(k)(s_(k)), where s_(k) may be the effective input to node k. Inparticular embodiments, the input of an activation functioncorresponding to a node may be weighted. Each node may generate outputusing a corresponding activation function based on weighted inputs. Inparticular embodiments, each connection between nodes may be associatedwith a weight. As an example and not by way of limitation, a connection125 between the node 105 and the node 115 may have a weightingcoefficient of 0.4, which may indicate that 0.4 multiplied by the outputof the node 105 is used as an input to the node 115. As another exampleand not by way of limitation, the output y_(k) of node k may bey_(k)=F_(k)(s_(k)), where F_(k) may be the activation functioncorresponding to node k, s_(k)=Σ_(j)(w_(jk)x_(j)) may be the effectiveinput to node k, x_(j) may be the output of a node j connected to nodek, and w_(jk) may be the weighting coefficient between node j and nodek. In particular embodiments, the input to nodes of the input layer maybe based on a vector representing an object. A vector representing anobject may also be referred to as an embedding representing the object.Although this disclosure describes particular inputs to and outputs ofnodes, this disclosure contemplates any suitable inputs to and outputsof nodes. Moreover, although this disclosure may describe particularconnections and weights between nodes, this disclosure contemplates anysuitable connections and weights between nodes.

In particular embodiments, an ANN may be trained using training data. Asan example and not by way of limitation, training data may compriseinputs to the ANN 100 and an expected output. As another example and notby way of limitation, training data may comprise vectors eachrepresenting a training object and an expected label for each trainingobject. In particular embodiments, training an ANN may comprisemodifying the weights associated with the connections between nodes ofthe ANN by optimizing an objective function. As an example and not byway of limitation, a training method may be used (e.g., the conjugategradient method, the gradient descent method, the stochastic gradientdescent) to backpropagate the sum-of-squares error measured as adistances between each vector representing a training object (e.g.,using a cost function that minimizes the sum-of-squares error). Inparticular embodiments, an ANN may be trained using a dropout technique.As an example and not by way of limitation, one or more nodes may betemporarily omitted (e.g., receive no input and generate no output)while training. For each training object, one or more nodes of the ANNmay have some probability of being omitted. The nodes that are omittedfor a particular training object may be different than the nodes omittedfor other training objects (e.g., the nodes may be temporarily omittedon an object-by-object basis). Although this disclosure describestraining an ANN in a particular manner, this disclosure contemplatestraining an ANN in any suitable manner.

Vector Spaces and Embeddings

FIG. 2 illustrates an example view of a vector space 200. The vectorspace 200 may also be referred to as a feature space or an embeddingspace. In particular embodiments, an object or an n-gram may berepresented in a d-dimensional vector space, where d denotes anysuitable number of dimensions. Although the vector space 200 isillustrated as a three-dimensional space, this is for illustrativepurposes only, as the vector space 200 may be of any suitable dimension.Each vector may comprise coordinates corresponding to a particular pointin the vector space 200 (i.e., the terminal point of the vector). As anexample and not by way of limitation, vectors 210, 220, and 230 may berepresented as points in the vector space 200, as illustrated in FIG. 2. In particular embodiments, a mapping from data to a vector may berelatively insensitive to small changes in the data (e.g., a smallchange in the data being mapped to a vector will still result inapproximately the same mapped vector). In particular embodiments,social-networking system 1160 may map objects of different modalities tothe same vector space or use a function jointly trained to map one ormore modalities to a feature vector (e.g., between visual, audio, text).Although this disclosure may describe a particular vector space, thisdisclosure contemplates any suitable vector space.

In particular embodiments, an n-gram may be mapped to a respectivevector representation, which may be referred to as a term vector or aterm embedding. As an example and not by way of limitation, n-grams t₁and t₂ may be mapped to vectors

and

in the vector space 200, respectively, by applying a function

defined by a dictionary, such that

=

(t₁) and

=

(t₂). As another example and not by way of limitation, a dictionarytrained to map text to a vector representation may be utilized, or sucha dictionary may be itself generated via training. As another exampleand not by way of limitation, a model, such as Word2vec, may be used tomap an n-gram to a vector representation in the vector space 200. Inparticular embodiments, an n-gram may be mapped to a vectorrepresentation in the vector space 200 by using a machine leaning model(e.g., a neural network). The machine-learning model may have beentrained using training data (e.g., a corpus of objects each comprisingn-grams). In particular embodiments, the machine-learning model may betrained using an objective function or a loss function (e.g., a functionthat is to be maximized or minimized over training data). As an exampleand not by way of limitation, a machine-learning model may be trained topredict an n-gram in a sentence given other n-grams in the sentence(e.g., a continuous bag-of-words model). As another example and not byway of limitation, a machine-learning model may be trained to predictother n-grams in a sentence given an n-gram in the sentence (e.g., askip-gram model). Although this disclosure describes representing ann-gram in a vector space in a particular manner, this disclosurecontemplates representing an n-gram in a vector space in any suitablemanner.

In particular embodiments, an object may be represented in the vectorspace 200 as a vector, which may be referred to as a feature vector oran object embedding. As an example and not by way of limitation, objectse₁ and e₂ may be mapped to vectors

and

in the vector space 200, respectively, by applying a function

, such that

=

(e₁) and

=

(e₂). In particular embodiments, an object may be mapped to a vectorbased on one or more properties, attributes, or features of the object,relationships of the object with other objects, or any other suitableinformation associated with the object. As an example and not by way oflimitation, a function

may map objects to vectors by feature extraction, which may start froman initial set of measured data and build derived values (e.g.,features). As an example and not by way of limitation, an objectcomprising a video or an image may be mapped to a vector by using analgorithm to detect or isolate various desired portions or shapes of theobject. Features used to calculate the vector may be based oninformation obtained from edge detection, corner detection, blobdetection, ridge detection, scale-invariant feature transformation, edgedirection, changing intensity, autocorrelation, motion detection,optical flow, thresholding, blob extraction, template matching, Houghtransformation (e.g., lines, circles, ellipses, arbitrary shapes), orany other suitable information. As another example and not by way oflimitation, an object comprising audio data may be mapped to a vectorbased on features such as a spectral slope, a tonality coefficient, anaudio spectrum centroid, an audio spectrum envelope, a Mel-frequencycepstrum, or any other suitable information. In particular embodiments,when an object has data that is either too large to be efficientlyprocessed or comprises redundant data, a function

may map the object to a vector using a transformed reduced set offeatures (e.g., feature selection). In particular embodiments, afunction

may map an object e to a vector

(e) based on one or more n-grams associated with object e. In particularembodiments, an object may be mapped to a vector by using amachine-learning model. In particular embodiments, the machine-learningmodel may be trained using an objective function or a loss function.Although this disclosure describes representing an object in a vectorspace in a particular manner, this disclosure contemplates representingan object in a vector space in any suitable manner.

In particular embodiments, the social-networking system 1160 maycalculate a similarity metric of vectors in vector space 200. Asimilarity metric may be a cosine similarity, a Minkowski distance, aMahalanobis distance, a Jaccard similarity coefficient, or any suitablesimilarity metric. As an example and not by way of limitation, asimilarity metric of

and

may be a cosine similarity

$\frac{\overset{harpoonup}{v_{1}} \cdot \overset{harpoonup}{v_{2}}}{{\overset{harpoonup}{v_{1}}}\mspace{11mu}{\overset{harpoonup}{v_{2}}}}.$As another example and not by way of limitation, a similarity metric of

and

may be a Euclidean distance ∥

−

∥. A similarity metric of two vectors may represent how similar the twoobjects or n-grams corresponding to the two vectors, respectively, areto one another, as measured by the distance between the two vectors inthe vector space 200. As an example and not by way of limitation, vector210 and vector 220 may correspond to objects that are more similar toone another than the objects corresponding to vector 210 and vector 230,based on the distance between the respective vectors. In particularembodiments, social-networking system 1160 may determine a cluster ofvector space 200. A cluster may be a set of one or more pointscorresponding to feature vectors of objects or n-grams in vector space200, and the objects or n-grams whose feature vectors are in the clustermay belong to the same class or have a relationship to one another(e.g., a semantic relationship, a visual relationship, a topicalrelationship, etc.). As an example and not by way of limitation, cluster240 may correspond to sports-related content and another cluster maycorrespond to food-related content. Although this disclosure describescalculating a similarity metric between vectors and determining acluster in a particular manner, this disclosure contemplates calculatinga similarity metric between vectors or determining a cluster in anysuitable manner.

More information on vector spaces, embeddings, feature vectors, andsimilarity metrics may be found in U.S. patent application Ser. No.14/949,436, filed 23 Nov. 2015, U.S. patent application Ser. No.15/286,315, filed 5 Oct. 2016, and U.S. patent application Ser. No.15/365,789, filed 30 Nov. 2016, each of which is incorporated byreference.

Location Information

In particular embodiments, the social-networking system 1160 maydetermine a geographic location (hereinafter also “location”) associatedwith an entity (e.g., a user, a concept, a place-entity, or a clientsystem 1130 associated with a user or concept). The location of anobject may be identified and stored as a street address (e.g., “1601Willow Road, Menlo Park, Calif.”), a set of geographic coordinates(e.g., latitude and longitude), a reference to another location orobject (e.g., “the coffee shop next to the train station”), a referenceto a map tile (e.g., “map tile 32”), or using another suitableidentifier. In particular embodiments, the location of an object may beprovided by a user of an online social network. As an example and not byway of limitation, a user may input his location by checking-in at thelocation or otherwise providing an indication of his location. Asanother example and not by way of limitation, a user may input thelocation of a concept (e.g., a place or venue) by accessing the profilepage for the concept and entering the location information (e.g., thestress address) of the concept.

In particular embodiment, the location of a client system 1130 equippedwith cellular, Wi-Fi, Global Positioning System (GPS), or other suitablecapabilities may be identified with geographic-positioning signals. Asan example and not by way of limitation, a mobile-client system 1130 mayinclude one or more sensors that may facilitate geo-locationfunctionalities of the system. Processing of sensor inputs by themobile-client system 1130 with one or more sensor devices (for example,processing a GPS sensor signal and displaying in the device's graphicaluser interface a map of a location corresponding to the GPS sensorsignal) may be implemented by a combination of hardware, software,and/or firmware (or device drivers). Geographic-positioning signals maybe obtained by cell tower triangulation, Wi-Fi positioning, or GPSpositioning. In particular embodiments, a geographic location of anInternet-connected computer can be identified by the computer's IPaddress. A mobile-client system 1130 may also have additionalfunctionalities incorporating geographic-location data of the device,such as, for example, providing driving directions, displaying a map ofa current location, or providing information of nearby points ofinterest such as restaurants, gas stations, etc. As an example and notby way of limitation, a web browser application on the mobile-clientsystem 1130 may access a mapping library (e.g., via a function call)that generates a map containing a GPS location obtained by a devicedriver interpreting a GPS signal from a GPS sensor, and display the mapin the web browser application's graphical user interface. In particularembodiments, the location of a user may be determined from a searchhistory associated with the user. As an example and not by way oflimitation, if a particular user has previously queried for objects in aparticular location, the social-networking system 1160 may assume thatthe user is still at that particular location. Although this disclosuredescribes determining the location of an object in a particular manner,this disclosure contemplates determining the location of an object inany suitable manner.

In particular embodiments, the social-networking system 1160 maymaintain a database of information relating to locations or places. Thesocial-networking system 1160 may also maintain meta information aboutparticular locations or places, such as, for example, photos of alocation or place, advertisements, user reviews, comments, “check-in”activity data, “like” activity data, hours of operation, or othersuitable information related to the location or place. In particularembodiments, a location or place may correspond to a concept node 1204in a social graph 1200. The social-networking system 1160 may allowusers to access information regarding a location or place using a clientapplication (e.g., a web browser or other suitable application) hostedby a client system 1130. As an example and not by way of limitation, thesocial-networking system 1160 may serve webpages (or other structureddocuments) to users that request information about a location or place.In addition to user profile and location information, the system maytrack or maintain other information about the user. As an example andnot by way of limitation, the social-networking system 1160 may supportgeo-social-networking functionality including one or more location-basedservices that record the user's location. As an example and not by wayof limitation, users may access the geo-social-networking system using aspecial-purpose client application hosted by a mobile-client system 1130of the user (or a web- or network-based application using a browserclient). The client application may automatically access GPS or othergeo-location functions supported by the client system 1130 and reportthe user's current location to the geo-social-networking system. Inaddition, the client application may support geo-social networkingfunctionality that allows users to “check-in” at various locations orplaces and communicate this location or place to other users. A check-into a given location or place may occur when a user is physically locatedat a location or place and, using a client system 1130, access thegeo-social-networking system to register the user's presence at thelocation or place. The social-networking system 1160 may automaticallycheck-in a user to a location or place based on the user's currentlocation and past location data. In particular embodiments, thesocial-networking system 1160 may allow users to indicate other types ofrelationships with respect to particular locations or places, such as“like,” “fan,” “worked at,” “recommended,” “attended,” or anothersuitable type of relationship. In particular embodiments, “check-in”information and other relationship information may be represented in thesocial graph 1200 as an edge 1206 connecting the user node 1202 of theuser to the concept node 1204 of the location or place.

Prediction of Next Place Visits

In particular embodiments, the social-networking system 1160 may predicta second geographic location that a user will visit subsequent to theuser's presence at a first geographic location. In other words, thesocial-networking system 1160 may predict the next place the user willvisit given information about a previous place the user has visited orwill visit. In particular embodiments, a technical problem arising inthe field of geo-location may be providing users with informationassociated with a geographic location they will visit in the futurebased on a current geolocation. The technical solutions described hereinof automatically predicting a second geographic location that a userwill visit subsequent to the user's presence at a first geographiclocation and sending the user information associated with the secondgeographic location may improve computing processes related to receivingand executing queries by reducing the need for the user to send queriesrelated to the second geographic location, which may reduce thecomputing resources needed for such processes. In particularembodiments, the social-networking system 1160 may calculate theprobability P (k IX) that a user will visit a second geographic locationk subsequent to the user's presence at given a first geographic locationX. The social-networking system 1160 may use a model trained by machinelearning, which may take an input embedding representing a firstplace-entity corresponding to a first geographic location and output apredicted second place-entity corresponding to a second geographiclocation that the user will subsequently visit. In particularembodiments, the prediction may be a most probable second geographiclocation that the user will subsequently visit, a confidence scorerepresenting the confidence that the user will subsequently visit aparticular second geographic location, or it may be a ranked list ofprobable second geographic locations that the user will subsequentlyvisit. In particular embodiments, features of a place-entity may beselected by feature extraction to generate an embedding representing aplace-entity. The features extracted may include the category of theplace-entity (e.g., bar, gym), the time of the user's visit to thegeographic location corresponding to the place-entity (e.g., time ofday, day of week), the hours that the geographic location correspondingto the place-entity is open, popular hours of a geographic locationcorresponding to the place-entity, or any other suitable featuresrelated to the place-entity. User attributes may also be used togenerate an embedding (e.g., an embedding of a place-entity may bereconstructed from embeddings of the users visiting the geographiclocation corresponding to the place-entity). In particular embodiments,training data used to train a machine-learning model may be from usersof an online social network or any other suitable data. To train amachine-learning model, positive and negative pairs of first geographiclocations and second geographic locations may be used. In particularembodiments, a machine-learning model used to generate place-entityembeddings may be evaluated. As an example and not by way of limitation,for an evaluation set, the mean-reciprocal rank (MRR) may be used toevaluate the machine-learning model. In particular embodiments, amachine-learning model may be used to make a prediction about whichsecond geographic location a user will visit to subsequent to a givencurrent first geographic location of the user. In particularembodiments, a prediction may be used to suggest a second geographiclocation a user should visit next. In particular embodiments, aprediction may be used to deliver content associated with the predictedsecond geographic location to the client system 1130 of the user. Inparticular embodiments, a prediction may be used to predict a user'scurrent second geographic location based on a first geographic locationpreviously visited by the user. As an example and not by way oflimitation, a user's current location may be unknown or unable to beprecisely determined. The social-networking system 1160 may use aprevious first geographic location visited by the user to predict auser's current second geographic location. In particular embodiments,the prediction may be used as one of many factors in predicting theuser's current geographic location. As an example and not by way oflimitation, the predicted current second geographic location may bebased on the output of a machine-learning model as well as GPS data,wireless signal-information received by the client system 1130 of theuser, or any other suitable information. Although this disclosuredescribes predicting a second geographic location or place-entity basedon a first geographic location or first place-entity in a particularmanner, this disclosure contemplates predicting a second geographiclocation or place-entity based on a first geographic location or firstplace-entity in any suitable manner. Furthermore, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

FIG. 3 illustrates example symbolic depictions of geographic locations302-308 on an example map 300. In particular embodiments, thesocial-networking system 1160 may receive, from a client system 1130associated with a first user, data indicating that the first user islocated at a first geographic location at a first time. A geographiclocation may refer to a particular physical place or location. As anexample and not by way of limitation, the social-networking system 1160may receive data indicating that a first user is located at geographiclocation 302. In particular embodiments, the data indicating that theuser is located at a first geographic location may comprise geographiccoordinates, an identification of a business at the geographic location,or any other suitable data. As an example and not by way of limitation,a client system 1130 of a user may send to the social-networking system1160 the geographical latitude and longitude of the client system 1130,which the client system 1130 may have determined using a GlobalPositioning System (GPS). As another example and not by way oflimitation, a user may ‘check-in’ to a STARBUCKS coffee house on anonline social network of the social-networking system 1160 and thecheck-in may comprise a unique ID associated with the STARBUCKS that maybe used to determine the geographic location of the STARBUCKS. Althoughthis disclosure describes receiving data indicating that a user islocated at a geographic location in a particular manner, this disclosurecontemplates receiving data indicating that a user is located at ageographic location in any suitable manner. Furthermore, although thisdisclosure describes particular data indicating a geographic location ofa user, this disclosure contemplates any suitable data indicating ageographic location of a user.

In particular embodiments, a machine-learning model may be trained togenerate an embedding representing a place-entity. A place-entity maycorrespond to a particular geographic location. As an example and not byway of limitation, a place-entity may correspond to a concept noderepresenting a particular geographic location. As another example andnot by way of limitation, a place-entity may comprise data or areference to data associated with a particular geographic location. Inparticular embodiments, the machine-learning model may be trained byaccessing a plurality of positive training pairs of trainingplace-entities, accessing a plurality of negative training pairs oftraining place-entities, and optimizing an objective function based onthe plurality of positive training pairs and the plurality of negativetraining pairs. Although this disclosure describes training amachine-learning model in a particular manner, this disclosurecontemplates training a machine-learning model in any suitable manner.

In particular embodiments, the social-networking system 1160 may accessa plurality of positive training pairs of training place-entities. Eachpositive training pair may comprise a first training place-entitycorresponding to a first training geographic location that a second userwas located at and a second training place-entity corresponding to asecond training geographic location that the second user was located atwithin the particular timeframe of the second user being located at thefirst training geographic location. As an example and not by way oflimitation, a particular timeframe may be a twenty-four hour timeframe.A second user may visit a WALGREENS pharmacy at a geographic locationcorresponding to place-entity A then visit a BURGER KING restaurant at ageographic location corresponding to place-entity B an hour later. Thepair of place-entities (A, B) may be a positive training pair ofplace-entities. Although this disclosure describes particular positivetraining pairs of place-entities, this disclosure contemplates anysuitable positive training pair of place-entities.

In particular embodiments, the social-networking system 1160 may accessa plurality of negative training pairs of training place-entities. Eachnegative training pair may comprise a first training place-entitycorresponding to a first training geographic location that a second userwas located at and a second training place-entity corresponding to asecond training geographic location that the second user was not locatedwithin the particular timeframe of the second user being located at thefirst training geographic location. As an example and not by way oflimitation, a particular timeframe may be a twelve-hour timeframe. Asecond user may visit a WALGREENS pharmacy at a geographic locationcorresponding to place-entity A. The second user may have not beenlocated at a MCDONALD'S restaurant at a geographic locationcorresponding to place-entity C within the twelve-hour timeframe fromthe user's visit to the WALGREENS pharmacy. The pair of place-entities(A, C) may be a negative training pair of place-entities. In particularembodiments, for each negative training pair, the second traininggeographic location may be within a predetermined geographic distance ofthe first training geographic location. As an example and not by way oflimitation, the predetermined geographic distance may be 25 miles, andthe geographic distance between the first training geographic locationand second training geographic location corresponding to each negativetraining pair may be less than or equal to 25 miles. Although thisdisclosure describes particular negative training pairs ofplace-entities, this disclosure contemplates any suitable negativetraining pair of place-entities.

In particular embodiments, the social-networking system 1160 may trainthe machine-learning model to generate an embedding representing a firstplace-entity and an embedding representing a predicted secondplace-entity by optimizing an objective function based on the pluralityof positive training pairs and the plurality of negative training pairs.As an example and not by way of limitation, the machine-learning modelmay be an artificial neural network (“ANN”). The social-networkingsystem 1160 may train the ANN by backpropagating the sum-of-squareserror based on the observed statistical frequency between the positivetraining pairs and negative training pairs. In particular embodiments,the objective function may comprise a cosine similarity between anembedding representing a first training place-entity and an embeddingrepresenting a second training place-entity. A cosine similarity betweenan embedding representing a first place-entity and an embeddingrepresenting a second place-entity may represent or correspond to aprobability or statistical likelihood that a user was located at thesecond geographic location corresponding to the second place-entitywithin the particular timeframe of the user being located at a firstgeographic location corresponding to the first place-entity. Althoughthis disclosure describes training a particular machine-learning modelin a particular manner, this disclosure contemplates training anysuitable machine-learning model in any suitable manner.

FIG. 4 illustrates an example calculation of an example evaluationmetric. An evaluation metric may be a statistical measure for evaluatinghow well a machine-learning model performs. As an example and not by wayof limitation, an evaluation metric may be a mean-reciprocal rank, amean-average precision, or any other suitable metric. In particularembodiments, a machine-learning model may be evaluated by accessing aplurality of evaluation pairs of evaluation place-entities, generating,for each first evaluation place-entity, an ordered list of predictedsecond place entities, and calculating, for each first evaluationplace-entity, a mean-reciprocal rank based on the second evaluationplace-entity and the ordered list of predicted second place entities. Inparticular embodiments, the social-networking system 1160 may access aplurality of evaluation pairs of evaluation place-entities. Eachevaluation pair may comprise a first evaluation place-entitycorresponding to a first evaluation geographic location that a seconduser was located at and a second evaluation place-entity correspondingto a second evaluation geographic location that the second user waslocated at within the particular timeframe of the second user beinglocated at the first evaluation geographic location. As an example andnot by way of limitation, for a particular timeframe of 24 hours, asecond user may visit a restaurant at a geographic locationcorresponding to place-entity A then visit a gym at a geographiclocation corresponding to place-entity R an hour later. The pair ofplace-entities (A, R) may be evaluation pairs, as shown in row 402. Inparticular embodiments, the social-networking system 1160 may generate,for each first evaluation place-entity, an ordered list of predictedsecond place-entities. As an example and not by way of limitation, for afirst evaluation place-entity A, an ordered list of predicted secondplace-entities {C, B, R} may be generated by the machine-learning model,where C may be the predicted most probable second-place entity among thelist of predicted second place-entities and R may be the predicted leastprobable second-place entity among the list of predicted secondplace-entities. In particular embodiments, the social-networking system1160 may calculate a mean-reciprocal rank associated with themachine-learning model. The mean-reciprocal rank may be based aplurality of reciprocal ranks corresponding to the plurality ofevaluation pairs. Each reciprocal rank corresponding to an evaluationpair may be calculated based on the first evaluation place-entity of theevaluation pair, the second evaluation place-entity of the evaluationpair, and the ordered list of predicted second place entitiescorresponding to the first evaluation place-entity of the evaluationpair. As an example and not by way of limitation, place-entities A, B,and C, may each be a first evaluation place-entity and may correspond torows 402, 404, and 406, respectively. Row 402 may show the calculationcorresponding to place-entity A. The generated ordered listcorresponding to place-entity A may be {C, B, R}. The correspondingevaluation pair may be (A, R). Because the second evaluationplace-entity R was the third predicted second place-entity in theordered list, row 402 may have a rank of 3 and a reciprocal rank of ⅓.Similar calculations may be performed for first evaluationplace-entities B and C, and shown in rows 404 and 406, respectively. Amean-reciprocal rank may be the mean of the reciprocal ranks, which maybe calculated as ⅓(⅓+1+½)= 11/18, or approximately 0.61. Although thisdisclosure describes a particular evaluation metric, this disclosurecontemplates any suitable evaluation metric. Furthermore, although thisdisclosure describes calculating a mean-reciprocal rank in a particularmanner for particular evaluation pairs, this disclosure contemplatescalculating a mean-reciprocal rank in any suitable manner for suitableevaluation pairs.

In particular embodiments, the social-networking system 1160 may accessa first embedding representing a first place-entity corresponding to thefirst geographic location. The first embedding may be a point in ad-dimensional embedding space. As an example and not by way oflimitation, a client system 1130 of a user may send data to thesocial-networking system 1160 indicating that the user is located at ageographical latitude of 37.417423 degrees and a geographical longitudeof −122.130573 degrees. The geographic location at these coordinates maybe a STARBUCKS coffee house, which may correspond to a place-entity. Thesocial-networking system 1160 may access an embedding representing aplace-entity corresponding to the STARBUCKS coffee house. Although thisdisclosure describes accessing an embedding representing a place-entityin a particular manner, this disclosure contemplates accessing anembedding representing a place-entity in any suitable manner.

In particular embodiments, the social-networking system 1160 may accessa plurality of second embeddings representing a plurality of predictedsecond place-entities, respectively. Each second place-entity maycorrespond to a second geographic location. Each second embedding may bea point in the d-dimensional embedding space. As an example and not byway of limitation, referencing FIG. 3 , the social-networking system1160 may receive data indicating that a first user is located atgeographic location 302. The social-networking system 1160 may access afirst embedding representing a first place-entity corresponding to firstgeographic location 302. The social-networking system 1160 may accesssecond embeddings, each representing the second place-entitiescorresponding to the respective second geographic locations 304, 306,and 308. Although this disclosure describes accessing an embeddingrepresenting a place-entity in a particular manner, this disclosurecontemplates accessing an embedding representing a place-entity in anysuitable manner.

FIG. 5 illustrates example features of example place-entities. Inparticular embodiments, the social-networking system 1160 may generate,using a machine-learning model, the first embedding and each of thesecond embeddings. In particular embodiments, the first embedding of thefirst place-entity may be generated based on features of the firstplace-entity. In particular embodiments, the plurality of secondembeddings of the plurality of predicted second place-entities may begenerated based on features of the respective second place-entities. Inparticular embodiments, the features of a place-entity may comprise acategory of the place-entity, the first time, the hours of operation ofa business located at a geographic location corresponding to theplace-entity, popular hours of a business located at geographic locationcorresponding to the place-entity (e.g., hours when the business has asurge in customers), or any combination thereof. As an example and notby way of limitation, referencing FIG. 3 , an embedding representing afirst place-entity corresponding to a geographic location 302 may begenerated. Referencing FIG. 5 , the first-place entity features 502 maybe the features of the first place-entity. In this example, the featuresusing to generate the embedding of the first-place entity may comprisethe hour and day of the week of the first time (6:34 pm on a Thursday),the geographic coordinates of geographic location 302 (37.421998°,−122.138187°), and the category of the first place-entity (gym). Inparticular embodiments, features-types of the features used to generatethe first embedding of the first place-entity may not be identical tofeature-types of the features used to generate the second embeddings ofthe second place-entities. As an example and not by way of limitation,the features used to generate a first embedding representing a firstplace-entity corresponding to the geographic location 302 may be thefirst-place entity features 502. The first-place entity features 502 maycomprise the feature-types time, day of the week, geographiccoordinates, and place-entity category. The features used to generatesecond embeddings representing a second place-entities corresponding tothe geographic locations 304, 306, and 308 may be the second-placeentity features 504, 506, and 508, respectively. The second-place entityfeatures 504, 506, and 508 may each comprise the feature-types time, dayof the week, geographic coordinates, check-in count, place-entitycategory, and hours of operation of a business at the respectivegeographic locations. The feature-types used to generate embeddingsrepresenting the second place-entities in this example may comprisecheck-in count and hours of operation, whereas the feature-types used togenerate the first place-entity may not. Although this disclosuredescribes particular features of particular place-entities, thisdisclosure contemplates any suitable features of any suitableplace-entities.

In particular embodiments, at least one second place-entity maycorrespond to a second geographic location within a predeterminedgeographic distance of the first geographic location. As an example andnot by way of limitation, a second-place entity may correspond to asecond geographic location that is located within 50 miles of the firstgeographic location. As another example and not by way of limitation,the social-networking system 1160 may select only predicted secondplace-entities that correspond to second geographic locations within 25miles of the first geographic location. As yet another example and notby way of limitation, referencing FIG. 3 , the geographic location 302may be the first geographic location and the geographic locations 304,306, and 308 may each be second geographic locations. The geographiclocations 304, 306, and 308 may each be within 20 miles of thegeographic location 302. In particular embodiments, at least one secondplace-entity may correspond to a second geographic location previouslyvisited by the user. In particular embodiments, at least one secondplace-entity may correspond to a second geographic location visited by asecond user, wherein the second user shares one or more attributes withthe first user. As an example and not by way of limitation, a first usermay be located at the geographic location 302. The first user may bebetween 18 and 24 years of age. The geographic location 304 may beselected as a second geographic location based on determining that asecond user between 18 and 24 years of age previously visited thegeographic location 304. Although this disclosure describes particulargeographic locations, this disclosure contemplates any suitablegeographic location. Furthermore, although this disclosure describesselecting particular geographic locations based on particular criteria,this disclosure contemplates selecting any suitable geographic locationsbased on any suitable criteria.

In particular embodiments, the social-networking system 1160 maycalculate, for each of the predicted second place-entities, a similaritymetric between the embedding representing the first place-entity and theembedding representing the second place-entity. The similarity metricmay correspond to a probability that the first user will be located atthe second geographic location corresponding to the second place entitywithin a particular timeframe of the first user being located at thefirst geographic location at the first time. As an example and not byway of limitation, an embedding

a may represent first place-entity A, and second embeddings

and

may represent second place-entities B and C, respectively. A cosinesimilarity between

and

may be 0.24 and a cosine similarity between

and

may be 0.86. This may indicate that there is a higher probability thatthe first user will be located at a geographic location corresponding toC than the probability that the user will be located at a geographiclocations corresponding to B within the particular timeframe. Althoughthis disclosure describes calculating a similarity metric in aparticular manner, this disclosure contemplates calculating a similaritymetric in any suitable manner.

In particular embodiments, the social-networking system 1160 may rankeach of the predicted second place-entities based on their calculatedsimilarity metrics. In particular embodiments, the social-networkingsystem 1160 may send, to the client system 1130, information associatedwith one or more second geographic locations corresponding to one ormore second place-entities having a ranking greater than a thresholdranking. As an example and not by way of limitation, the first user maybe located at geographic location A. Second place-entities B, C, D, andE, may be ranked {C, D, B, E}. Information associated with secondplace-entities C and D may be sent to the client system 1130 of thefirst user based on the ranking of C and D being above a threshold rank.Although this disclosure describes ranking place-entities and sendinginformation in a particular manner, this disclosure contemplates rankingplace-entities and sending information in any suitable manner.

FIG. 6 illustrates an example method 600 for predicting a secondgeographic location that a user will visit subsequent to the user'spresence at a first geographic location. The method may begin at step610, where the social-networking system 1160 may receive, from a clientsystem 1130 associated with a first user of the online social network,data indicating that the first user is located at a first geographiclocation at a first time. At step 620, the social-networking system 1160may access a first embedding representing a first place-entitycorresponding to the first geographic location, wherein the firstembedding is a point in a d-dimensional embedding space. At step 630,the social-networking system 1160 may access a plurality of secondembeddings representing a plurality of predicted second place-entities,respectively, each second place-entity corresponding to a secondgeographic location, wherein each second embedding is a point in thed-dimensional embedding space. At step 640, the social-networking system1160 may calculate, for each of the second place-entities, a similaritymetric between the embedding representing the first place-entity and theembedding representing the second place-entity, wherein the similaritymetric corresponds to a probability that the first user will be locatedat the second geographic location corresponding to the second placeentity within a particular timeframe of the first user being located atthe first geographic location at the first time. At step 650, thesocial-networking system 1160 may rank each of the second place-entitiesbased on their calculated similarity metrics. At step 660, thesocial-networking system 1160 may send, to the client system 1130,information associated with one or more second geographic locationscorresponding to one or more second place-entities having a rankinggreater than a threshold ranking. Particular embodiments may repeat oneor more steps of the method of FIG. 6 , where appropriate. Although thisdisclosure describes and illustrates particular steps of the method ofFIG. 6 as occurring in a particular order, this disclosure contemplatesany suitable steps of the method of FIG. 6 occurring in any suitableorder. Moreover, although this disclosure describes and illustrates anexample method for predicting a second geographic location that a userwill visit subsequent to the user's presence at a first geographiclocation including the particular steps of the method of FIG. 6 , thisdisclosure contemplates any suitable method for predicting a secondgeographic location that a user will visit subsequent to the user'spresence at a first geographic location including any suitable steps,which may include all, some, or none of the steps of the method of FIG.6 , where appropriate. Furthermore, although this disclosure describesand illustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 6 , this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 6 .

Location Prediction Using Wireless Signals

In particular embodiments, the social-networking system 1160 maydetermine the most likely geographic location a user is located based onbackground signal-information from a first software application of aclient system 1130 of the user and a places-API call from a secondsoftware application of the client system 1130. In particularembodiments, a technical problem arising in the field of geo-locationmay be to disambiguate the geographic location of a user. In particularembodiments, using background signal-information, as described in thisdisclosure, may be used as an alternative to or in conjunction withother location data (e.g., GPS), which may provide the advantage of moreaccurately or precisely determining a geographic location of a user. Asan example and not by way of limitation, a user may be located at “TheOld Pro.” A first software application may collect and send to thesocial-networking system 1160 background signal-information associatedwith the geographic location (e.g., Wi-Fi signals at that location). Thesignal-information and a first client identifier of the client system1130 may be stored in a signal-information database. The first clientidentifier may be hashed. The user may check-in to “The Old Pro” using asecond software application. The second software application maycheck-in by using a places-API call of an online social network of thesocial-networking system 1160 to access information associated with aplace-entity corresponding to the geographic location “The Old Pro.” Theplace-entity and first client identifier may be recorded in an API-calllog. The first client identifier may be hashed. The social-networkingsystem 1160 may determine a correlation between the signal-informationand the place-entity corresponding to “The Old Pro” based on matchingthe hash of the first client identifier in the signal-information logand the hash of the first client identifier in the API-call log. Thesocial-networking system 1160 may update a place-entity database toindicate that the place-entity corresponding to “The Old Pro”corresponds to the background signal-information. A second client system1130 of a second user may send background signal-information. Thesocial-networking system 1160 may determine that the second clientsystem 1130 is located at “The Old Pro” based on determining that thebackground signal-information from the second client system 1130 matchesthe background signal-information from the first client system 1130.Although this disclosure describes determining a most likely geographiclocation of a user or a client system 1130 in a particular manner, thisdisclosure contemplates determining a most likely geographic location ofa user or a client system 1130 in any suitable manner. Furthermore,although this disclosure describes or illustrates particular embodimentsas providing particular advantages, particular embodiments may providenone, some, or all of these advantages.

In particular embodiments, a geographic location may refer to adistinct, identifiable, or defined location (e.g., a particularrestaurant, a particular location within a store e.g. the checkoutregister, an airport, a park, etc.). In particular embodiments, ageographic location may correspond to a place-entity. A place-entity maycomprise data associated with a geographic location corresponding to theplace-entity. In particular embodiments, a place-entity may becorrespond to a node in a social graph (e.g., a concept node). Althoughthis disclosure describes particular geographic locations andplace-entities, this disclosure contemplates any suitable geographiclocation and any suitable place-entities.

In particular embodiments, the social-networking system 1160 mayreceive, from a first software application of a first client system 1130associated with a first user of the online social network, backgroundsignal-information. The background signal-information may identify oneor more first wireless signals within wireless communication range ofthe first client system 1130. In particular embodiments, at least one ofthe wireless signals may be a Wi-Fi signal, a Bluetooth signal, acellular signal, a mobile phone signal, or a near-field communicationsignal. As an example and not by way of limitation, a user may be in acoffee shop. The coffee shop may provide Wi-Fi access via a wirelessaccess point. The client system 1130 may receive a Wi-Fi signal from theWi-Fi access point. Further, the client system 1130 may receive a mobilephone signal from a cellular network. A first software application ofthe client system 1130 may send information to the social-networkingsystem 1160 associated with the Wi-Fi signal and the mobile phonesignal. In particular embodiments, the first software application may beassociated with an online social network of the social-networking system1160. The online social network may have permission to access thesignal-information. As an example and not by way of limitation, the userof the client system 1130 may grant the online social network permissionto gather, access, and/or transmit background signal-information. Inparticular embodiments, the signal-information may comprise anidentifier of one or more sources of the one or more wireless signals.As an example and not by way of limitation, an identifier of a source ofa Wi-Fi signal may be a service set identifier (SSID), a media accesscontrol (MAC) address, or any other suitable identifier. As anotherexample and not by way of limitation, an identifier of a source of amobile phone signal may be a cellular system identification number(SID), a network identity number (NID), a cell identifier (CID), or anyother suitable identifier. In particular embodiments, thesignal-information may comprise a signal strength of the one or morewireless signals. As an example and not by way of limitation,signal-information associated with a mobile phone signal may comprisethe signal strength of the mobile phone signal indicated by thetransmitted power output as received by an antenna of the client systemmeasured as decibel-microvolts per meter (dBμV/m) above a referencelevel of one milliwatt (dBm). Although this disclosure describesparticular background signal-information identifying particular wirelesssignals in a particular manner, this disclosure contemplates anysuitable background signal-information identifying any suitable wirelesssignal in any suitable manner.

In particular embodiments, the social-networking system 1160 may storethe signal-information and a first client identifier for the firstclient system 1130 in a signal-information database. As an example andnot by way of limitation, the signal-information and the first clientidentifier may be stored in the same row of a relational database. Inparticular embodiments, the social-networking system 1160 may receive,from the client system 1130, the first identifier of the client system1130. In particular embodiments, the first client identifier may behashed. As an example and not by way of limitation, thesocial-networking system 1160 may receive a unique client identifier forthe client system 1130, input the unique identifier for the clientsystem into a cryptographic hash function, and store the hashed firstclient identifier in the signal-information database. Thesocial-networking system 1160 may store only the hashed identifier,which may preserve anonymity of the user and may have the effect of notretaining personally identifiable information associated with the user.In particular embodiments, the cryptographic hash function may changeperiodically. As an example and not by way of limitation, thecryptographic hash function may change daily. In particular embodiments,applying the cryptographic hash function to the first client identifiermay comprise adding salt to the first client identifier (e.g., addingrandom data to the output of the hash function). Although thisdisclosure describes storing signal-information and hashing anidentifier in a particular manner, this disclosure contemplates storingsignal-information and hashing an identifier in any particular manner.

In particular embodiments, the social-networking system 1160 mayreceive, from a second software application of the first client system1130 via a places-application programming interface (places-API) of theonline social network, a places-API call indicating that the firstclient system 1130 is located at a geographic location corresponding toa first place-entity. The second software application may be separatefrom the first software application. In particular embodiments, theplaces-API may comprise a set of instructions defined at the onlinesocial network, the set of instructions being executable to enable thesecond software application to request information about place-entitiesand send location information to the online social network. Inparticular embodiments, the places-API call may comprise a labelassociated with the first place-entity. The places-API call may comprisea social action associated with the first place-entity. As an exampleand not by way of limitation, an online social network of thesocial-networking system 1160 may provide a places-API. The secondsoftware application of the user's client system 1130 may bephoto-sharing application. The user may be located at the sports bar“The Old Pro” and may use the second software application to take ofphotograph of food using the second software application. The user may“tag” the photo as having been taken at “The Old Pro.” The secondsoftware application may execute a places-API call using the places-APIprovided by the social-networking system 1160. The places-API call maycomprise information indicating that the user is located at “The OldPro.” Although this disclosure describes a particular API and particularinformation indicating that a client system is located at a geographiclocation, this disclosure contemplates any suitable API and any suitableinformation indicating that a client system is located at a geographiclocation.

In particular embodiments, the social-networking system 1160 may recordthe places-API call in an API-call log. The API-call log may record thefirst place-entity and the first client identifier for the first clientsystem 1130. As an example and not by way of limitation, the firstplace-entity and the first identifier of the client system 1130 may bestored in the same row of a relational database. In particularembodiments, the first client identifier may be hashed. As an exampleand not by way of limitation, the social-networking system 1160 mayreceive a unique client identifier for the client system 1130, input theunique identifier for the client system 1130 into a cryptographic hashfunction, and store the hashed first client identifier in thesignal-information database. The social-networking system 1160 may storeonly the hashed identifier, which may preserve anonymity of the user andmay have the effect of not retaining personally identifiable informationassociated with the user. In particular embodiments, the cryptographichash function may change periodically. As an example and not by way oflimitation, the cryptographic hash function may change daily. Inparticular embodiments, the applying the cryptographic hash function tothe first client identifier may comprise adding salt to the first clientidentifier (e.g., adding random data to the output of the hashfunction). Although this disclosure describes storing an API call, placeentity, or identifier in a particular manner, this disclosure storing anAPI call, place entity, or identifier any particular manner.Furthermore, although this disclosure describes hashing an identifier ina particular manner, this disclosure contemplates hashing an identifierin any suitable manner.

In particular embodiments, the social-networking system 1160 maydetermine a correlation between the signal-information and the firstplace-entity. The correlation may be determined by comparing thesignal-information database and the API-call log to determine that thehash of the first client identifier in the signal-information logmatches the hash of the first client identifier in the API-call log. Inparticular embodiments, the hash of the client identifier recorded inthe API-call log may be the same as the hash of the client identifierrecorded in the signal-information database (e.g., the samecryptographic hash function may be applied). This may allow acorrelation between the record in the API-call log and the record in thesignal-information database while maintaining the anonymity or privacyof the user associated with the client system 1130. As an example andnot by way of limitation, a signal-information database may contain arecord {C₁, S₁} and the API-call log may contain the record {C₁, P₁},where C₁ may be the hash of the first client identifier, S₁ may be thesignal-information, and P₁ may be the first place-entity. By determiningthat C₁ is the same hash of the client identifier, a correlation betweenS₁ and P₁ may be determined. In particular embodiments, privacy of theuser may be maintained by receiving data from the first softwareapplication and second software application subject to a privacy check(e.g., checking whether the user has enabled or allowed sharing of thedata). In particular embodiments, privacy of the user may be maintainedby logging data received from the first software application and secondsoftware application subject to a privacy check (e.g., checking whetherthe user has enabled or allowed logging of the data). In particularembodiments, the correlation is determining based on the time elapsedbetween receiving the signal-information and receiving the places-APIcall. As an example and not by way of limitation, a correlation may bedetermined between a signal-information database record {C₁, S₁} and anAPI-call log may contain the record {C₁, P₁} when the signal-informationand the places-API call were received within 10 minutes of one another.Although this disclosure describes determining a particular correlationin a particular manner, this disclosure contemplates determining anysuitable correlation in any suitable manner.

In particular embodiments, the social-networking system 1160 may updatea place-entity database to indicate that the first place-entitycorresponds to the one or more first wireless signals identified by thesignal-information. As an example and not by way of limitation, acorrelation between S₁ and P₁ may be determined, where S₁ may be thesignal-information, and P₁ may be the first place-entity. Thesignal-information S₁ may identify one or more first wireless signalsW₁. The social-networking system 1160 may update a place-entity databaseby adding an entry {P₁, W₁}, which may indicate that that the firstplace-entity P₁ corresponds to the one or more first wireless signalsW₁. Although this disclosure describes updating a database to indicate acorrespondence in a particular manner, this disclosure contemplatesupdating a database to indicate a correspondence in any suitable manner.

FIG. 7 illustrates an example map graphically representing examplegeographic locations 702-718. In this example, the point 720 mayindicate a current location of a user. The user in this example may belocated at a geographic location 714. In particular embodiments, onetechnical problem in the field of geo-location may be to disambiguatethe geographic location of a user. As an example and not by way oflimitation, the client system 1130 of the user may use a GPS signal todetermine its approximate location. In the example of FIG. 7 , theclient system 1130 may determine its location to be approximately withinthe area indicated by area 720. Determining location by GPS may beuncertain due to circumstances such as the geometry of the GPSsatellite, blockage of the GPS signal, atmospheric conditions, designquality of the GPS receiver of the client system 1130, or other suchfactors. In the example of FIG. 7 , the GPS signal may be unable toaccurately determine that the user is located at geographic location714, as geographic locations 702, 704, 710, 712, 716, and 718 are alsowithin area 720. In particular embodiments, using backgroundsignal-information, as described in this disclosure, may be used as analternative to or in conjunction with other location data (e.g., GPS),which may provide the advantage of more accurately or preciselydetermining a geographic location of a user. Although this disclosuredescribes or illustrates particular embodiments as providing particularadvantages, particular embodiments may provide none, some, or all ofthese advantages.

In particular embodiments, the social-networking system 1160 may useinformation stored in the place-entity database to determine a locationof a second user. In particular embodiments, the social-networkingsystem 1160 may receive, from a second client system 1130 of a seconduser, background signal-information identifying one or more secondwireless signals within wireless communication range of the secondclient system 1130. As an example and not by way of limitation,referencing FIG. 7 , the second user may be located at a geographiclocation 714, indicated by point 720. The geographic location 714 may bea café with a Wi-Fi router. The Wi-Fi router of the geographic location714 may broadcast a Wi-Fi signal with an SSID of “Cool Café Wifi” with asignal strength of −35 dBm relative to the client system 1130. Thegeographic location 710 may broadcast a Wi-Fi signal via an access pointwith an SSID of “Other Business Wifi” with a signal strength of −67 dBmrelative to the client system 1130. The social-networking system 1160may receive, from the second client system 1130 of the second user,background signal-information identifying the wireless signals from theWi-Fi router of the geographic location 714 and the access point of thegeographic location 710. The background signal-information may comprisethe SSIDs and signal strength of the wireless signals. Although thisdisclosure describes receiving particular background signal-informationcomprising particular information from a user located at a particulargeographic location, this disclosure contemplates receiving any suitablebackground signal-information comprising any suitable information from auser located at any suitable geographic location.

In particular embodiments, the social-networking system 1160 maydetermine that the one or more second wireless signals match the one ormore first wireless signals. As an example and not by way of limitation,the one or more wireless signals W₁ may be represented in the form{SSID, dBm}. In this example, W₁ may be:

{{Cool Café Wifi, −35 dBm}, {Other Business Wifi, −65 dBm}, {Far Wifi,−88 dBm}}.

The social-networking system 1160 may receive from the second clientsystem signal-information S₂ identifying wireless signals W₂, which maybe:

{{Cool Café Wifi, −35 dBm}, {Other Business Wifi, −67 dBm}}.

The social-networking system 1160 may determine that W₁ and W₂ matchbased on a similarity between the SSIDs and the signal strengths of thewireless signals W₁ and W₂. In this example, a match between wirelesssignals may be determined when the SSIDs of W₁ and W₂ are identical andthe corresponding signal strengths are within a threshold amount of oneanother. The social-networking system 1160 may determine that {Far Wifi,−88 dBm} of W₁ has a low signal strength that W₁ and W₂ may matchdespite W₂ containing no corresponding wireless signal information(e.g., the second client system 1130 may not receive the wireless signaldue to the low-signal strength). Although this disclosure describesmatching one or more particular wireless signals in a particular manner,this disclosure contemplates matching any suitable one or more wirelesssignals in any suitable manner.

In particular embodiments, the social-networking system 1160 maydetermine that the second client system 1130 is located at a geographiclocation associated with the first place-entity based on the indicationin the place-entity database that the one or more first wireless signalscorrespond to the first place-entity. As an example and not by way oflimitation, the social-networking system 1160 may determine that the oneor more first wireless signals W₁ match the one or more second wirelesssignals W₂. The place-entity database may have been updated with theentry the place-entity database may have been updated with the entry{P₁, W₁}, which may indicate that that the first place-entity P₁corresponds to the one or more first wireless signals W₁. Thesocial-networking system 1160 may determine that the second user islocated at the geographic location corresponding to the firstplace-entity P₁ based on the entry {P₁, W₁} in the place-entity databaseand the determination that W₁ and W₂ match. In particular embodiments,determining that the second client system 1130 is located at thegeographic location associated with the first place-entity may befurther based on location data received from the second client system1130. As an example and not by way of limitation, referencing FIG. 7 ,other location data may indicate that the second user is located withinthe area 722. Determining a match between W₂ may comprise finding theclosest match between W₂ and wireless signals corresponding togeographic locations within the area 722. Although this disclosuredescribes determining a location of a client system 1130 in a particularmanner, this disclosure contemplates determining a location of a clientsystem 1130 in any suitable manner.

In particular embodiments, the social-networking system 1160 may send,to the second client system 1130, information associated with the firstplace-entity. As an example and not by way of limitation, thesocial-networking system 1160 may send a prompt to perform a socialaction associated with the first-place entity (e.g., a prompt to “like”or “check in” at a business associated with the first-place entity). Asanother example and not by way of limitation, the social-networkingsystem 1160 may send information associated with products sold at abusiness associated with the first-place entity. Although thisdisclosure describes sending particular information in a particularmanner, this disclosure contemplates sending any suitable information inany suitable manner.

FIG. 8 illustrates an example method 800 for correlatingsignal-information and place-entities. The method may begin at step 810,where the social-networking system 1160 may receive, from a firstsoftware application of a first client system 1130 associated with afirst user of the online social network, background signal-informationidentifying one or more first wireless signals within wirelesscommunication range of the first client system 1130, wherein the firstsoftware application is associated with the online social network, andwherein the online social network has permission to access thesignal-information. At step 820, the social-networking system 1160 maystore the signal-information and a first client identifier for the firstclient system in a signal-information database, wherein the first clientidentifier is hashed. At step 830, the social-networking system 1160 mayreceive, from a second software application of the first client systemvia a places-application programming interface (places-API) of theonline social network, a places-API call indicating that the firstclient system is located at a geographic location corresponding to afirst place-entity, wherein the second software application is separatefrom the first software application. At step 840, the social-networkingsystem 1160 may record the places-API call in an API-call log, whereinthe API-call log records the first place-entity and the first clientidentifier for the first client system 1130, and wherein the firstclient identifier is hashed. At step 850, the social-networking system1160 may determine a correlation between the signal-information and thefirst place-entity by comparing the signal-information database and theAPI-call log to determine that the hash of the first client identifierin the signal-information log matches the hash of the first clientidentifier in the API-call log. At step 860, the social-networkingsystem 1160 may update a place-entity database to indicate that thefirst place-entity corresponds to the one or more first wireless signalsidentified by the signal-information. Particular embodiments may repeatone or more steps of the method of FIG. 8 , where appropriate. Althoughthis disclosure describes and illustrates particular steps of the methodof FIG. 8 as occurring in a particular order, this disclosurecontemplates any suitable steps of the method of FIG. 8 occurring in anysuitable order. Moreover, although this disclosure describes andillustrates an example method for correlating signal-information andplace-entities including the particular steps of the method of FIG. 8 ,this disclosure contemplates any suitable method for correlatingsignal-information and place-entities including any suitable steps,which may include all, some, or none of the steps of the method of FIG.8 , where appropriate. Furthermore, although this disclosure describesand illustrates particular components, devices, or systems carrying outparticular steps of the method of FIG. 8 , this disclosure contemplatesany suitable combination of any suitable components, devices, or systemscarrying out any suitable steps of the method of FIG. 8 .

Cross-Validating Places

In particular embodiments, the social-networking system 1160 mayidentify place-entities corresponding to invalid geographic locationsbased on information associated with place-entity nodes eachcorresponding to the geographic location. In particular embodiments,information associated with a place-entity in a place-entities graph maybe from a source such as user-created pages, pages from the internet,WIKIPEDIA, from users via the Open Graph protocol, or any other suitablesource. In particular embodiments, a technical problem arising in thefield of place-entities graphs may be providing users with informationassociated with valid place-entities. Because information aboutplace-entities may originate from many sources, some of which havehigher quality information than others, information about place-entitiesmay include low-quality or invalid information. In particularembodiments, low-quality or invalid place-entities may be automaticallydetected and filtered (e.g., place-entities corresponding to ageographic location that does not exist, duplicate place-entities,non-public or user-specific places, etc.). The technical solutionsdescribed herein of automatically detecting and filtering invalidplace-entities may improve computing processes related to storing andsearching for place-entities, which may reduce the computing resourcesneeded for such processes while still providing high quality informationto users in an automatic manner. Furthermore, the technical solutionsdescribed herein may reduce or eliminate the need to manually reviewplace-entities to determine whether the place-entities are valid and mayreduce the data throughput required to respond to a query by filteringout invalid place-entity results. In particular embodiments, aplace-entity cluster may comprise a plurality of place-entity nodescorresponding to a plurality of respective place-entities, eachplace-entity corresponding to the same geographic location. Inparticular embodiments, using information from multiple place-entitynodes in a place-entity cluster may provide information that indicateswhether the place-entity nodes correspond to an invalid geographiclocation. A cluster-quality score may be calculated for a place-entitycluster based on a plurality of embeddings representing a plurality ofrespective place-entities of the place-entity cluster. In particularembodiments, place-entity nodes of a place-entity cluster may be flaggedas being of high quality, and may be used in a “premium” or “verified”place-entity graph based on determining that the cluster-quality scoreis above a threshold score. In particular embodiments, place-entitynodes of a place-entity cluster associated with a cluster-quality scorebelow a threshold score may be reclassified as a non-place-entity node,suppressed or filtered during a search, or ranked lower in a search.Although this disclosure describes determining whether a place-entitycorresponds to a valid or invalid geographic location in a particularmanner, this disclosure contemplates determining whether a place-entitycorresponds to a valid or invalid geographic location in any suitablemanner. Furthermore, although this disclosure describes or illustratesparticular embodiments as providing particular advantages, particularembodiments may provide none, some, or all of these advantages.

In particular embodiments, the social-networking system 1160 may accessa place-entities graph comprising a plurality of place-entity nodes. Inparticular embodiments, a place-entities graph may represent a pluralityof place-entity nodes each corresponding to a place-entity having aparticular geographic location. In particular embodiments, aplace-entity node may be a concept node 1204. Each place-entity node mayrepresent a place-entity corresponding to a particular geographiclocation. Each edge between two nodes may establish a single degree ofseparation between them. Although this disclosure describes particularplace-entity nodes in a particular place-entities graph, this disclosurecontemplates any suitable place-entity nodes in any suitableplace-entities graph.

FIG. 9 illustrates an example place-entity cluster 900. In particularembodiments, the social-networking system 1160 may identify aplace-entity cluster within the place-entities graph. The place-entitycluster may comprise a plurality of place-entity nodes corresponding toa plurality of place-entities, respectively. Each place-entity of aplace-entity cluster may correspond to the same geographic location. Inparticular embodiments, each place-entity cluster may compriseplace-entity nodes having duplication-values with respect to a canonicalplace-entity node in the place-entity cluster above a thresholdduplication-value. In particular embodiments, a deduplication processmay identify similar or duplicate place-entity nodes and createplace-entity clusters based on the identification. A pairwiseduplication comparison may be performed to generate a duplication-scorefor a particular pair of place-entity nodes. A determination ofduplicate place-entities may be a determination that two or moreplace-entity nodes are referring to the same place-entity or geographiclocation. As an example and not by way of limitation, a place-entitynode “New York City” and a place-entity node “I Heart NYC” may bothrefer to the geographic location of the city of New York, N.Y. Asanother example and not by way of limitation, referencing FIG. 9 ,cluster 900 may comprise place-entity nodes 902-908. Place-entity nodes902-908 may each refer to a geographic location associated with arestaurant called “The Golden Goose.” In particular embodiments,place-entity clusters may be created based on the duplication-scores,where a particular cluster may correspond to the same place-entity. Inparticular embodiments, a place-entity cluster may include canonicalplace-entity, as determined by a best-page selection process. As anexample and not by way of limitation, an official page ofsocial-networking system 1160 for “New York City” may be recognized asthe canonical place-entity for New York City, N.Y., while a page titled“The City That Never Sleeps” may be determined to be a lower-quality,duplicate place-entity. Although this disclosure describes particularplace-entities and particular place-entity clusters, this disclosurecontemplates any suitable place entities and any suitable place-entityclusters. Furthermore, although this disclosure describes clusteringplace-entity nodes, deduplication of place-entity nodes, and identifyingcanonical place-entity nodes in a particular manner, this disclosurecontemplates clustering place-entity nodes, deduplication ofplace-entity nodes, and identifying canonical place-entity nodes in anysuitable manner.

More information on geo-location, place-entities, place-entity nodes,place-entities graphs, deduplication, and canonical nodes may be foundin U.S. patent application Ser. No. 15/192,702, filed 24 Jun. 2016,which is incorporated by reference.

In particular embodiments, the social-networking system 1160 may accessa plurality of embeddings representing the plurality of place-entitiescorresponding to the place-entity cluster, respectively. Each embeddingmay be a point in a d-dimensional embedding space. In particularembodiments, for each place-entity of the plurality of place-entities,the social-networking system 1160 may generate the embeddingrepresenting the place-entity. For each place-entity, the embeddingrepresenting the place-entity may be generated based at least in part onone or more of a source of information associated with the place-entity,a date of information associated with the place-entity, an accuracy ofattributes of the place-entity, a number of photos associated with theplace-entity, an amount of content associated with the place-entity, arecency-value associated with the content or photos, or a number ofsocial signals associated with the place-entity. In particularembodiments, the source of information associated with at least oneplace-entity may be a user of the online social network or a third-partysystem 1170. In particular embodiments, each embedding representing aplace-entity may be generated based at least in part on whether theplace-entity has an entity-quality score less than a thresholdentity-quality score. As an example and not by way of limitation,referencing FIG. 9 , place entity-node 902 may correspond to aplace-entity that was created by a user, has 14 associated photos, andwas created 3 weeks prior to generating an embedding representing theplace-entity. An embedding representing the place-entity correspondingto place-entity node 902 may be generated based on the source of theplace-entity, the number of photos associated with the place-entity, anda recency-value. Although this disclosure describes accessing orgenerating an embedding representing a place-entity in a particularmanner, this disclosure contemplates accessing or generating anembedding representing a place-entity in any suitable manner.

In particular embodiments, the social-networking system 1160 maycalculate, for each place-entity, an entity-quality score of theplace-entity. In particular embodiments, the entity-quality score may becalculated by using a machine-learning model. As an example and not byway of limitation, place-entities with a greater number of associatedphotos may receive a higher quality-score than place-entities with alower number of associated photos. As another example and not by way oflimitation, place-entities with a higher number of number of socialsignals associated with the place-entity (e.g., check-ins, likes, etc.)may receive a higher quality-score than place-entities with a lowernumber of social signals associated with the place-entity. Although thisdisclosure describes calculating an entity-quality score in a particularmanner, this disclosure contemplates calculating an entity-quality scorein any suitable manner.

In particular embodiments, the social-networking system 1160 maycalculate, using a machine-learning model, a cluster-quality score ofthe place-entity cluster based on the plurality of embeddingsrepresenting the place-entities corresponding to the place-entitycluster. As an example and not by way of limitation, a cluster-qualityscore for a place-entity cluster comprising place-entities with a higheraverage quality-score may be higher than a place-entity clustercomprising place-entities with a higher average quality-score. Asanother example and not by way of limitation, a cluster-quality scorefor a place-entity cluster comprising more place-entities may be higherthan a place-entity cluster comprising fewer place-entities. Inparticular embodiments, the cluster-quality score may represent aprobability that the place-entities corresponding to the place-entitycluster correspond to a valid geographic location. In particularembodiments, a valid geographic location may refer to a geographiclocation that meets particular criteria. As an example and not by way oflimitation, a valid geographic location may be a geographic locationthat actually exists and is the location of a business open to thepublic. As another example and not by way of limitation, a user maycreate a place-entity called “Love,” which may correspond to the conceptof love rather than an actual geographic location. The place-entity“Love” may be identified as corresponding to an invalid geographiclocation. Although this disclosure describes calculating acluster-quality score in a particular manner, this disclosurecontemplates calculating a cluster-quality score in any suitable manner.

In particular embodiments, the social-networking system 1160 mayidentify the place-entities corresponding to the place-entity cluster ascorresponding to an invalid geographic location based on a determiningthat the cluster-quality score is less than a threshold cluster-qualityscore. As an example and not by way of limitation, a cluster-qualityscore may range from 0 to 1. A threshold cluster-quality score may be0.6. Referencing FIG. 9 , place-entity cluster 900 may have acluster-quality score of 0.5. Based on determining that thecluster-quality score of place-entity cluster 900 is below the thresholdcluster-quality, the place-entities corresponding to place-entity nodes902-908 may be identified as corresponding to an invalid location.Although this disclosure describes identifying place-entities ascorresponding to an invalid geographic location in a particular manner,this disclosure contemplates identifying place-entities as correspondingto an invalid geographic location in any suitable manner.

In particular embodiments, the social-networking system 1160 mayreceive, from a client system 1130 associated with a user, a querycomprising one or more n-grams. In particular embodiments, thesocial-networking system 1160 may identify one or more place-entitiesmatching at least a portion of the query. The identified place-entitiesmay comprise at least one place-entity corresponding to the place-entitycluster. In particular embodiments, the social-networking system 1160may calculate, for each identified place-entity, a relevance score. Inparticular embodiments, the social-networking system 1160 may rank theidentified place-entities based at least in part on their respectiverelevance scores. Each identified place-entity corresponding to theplace-entity cluster may be ranked lower than each identifiedplace-entity not corresponding to the place-entity cluster. Inparticular embodiments, the social-networking system 1160 may send, tothe client system 1130 responsive to the query, instructions forpresenting a search-results page, the search-results page comprising oneor more search results corresponding to the one or more identifiedplace-entities, respectively. The search-results page displays thesearch results ordered by the rank of the corresponding identifiedplace-entities. As an example and not by way of limitation, a user maysubmit a query comprising the n-gram “hope”. A place-entity “Hope forthe future” and a place-entity “Hope, British Columbia, Canada” may beidentified. The place-entity “Hope for the future” may correspond to theplace-entities cluster and may be identified as corresponding to aninvalid geographic location. The place-entity “Hope, British Columbia,Canada” may not correspond to the place-entities cluster and may becorrespond to a valid geographic location (e.g., a municipality). Theplace-entity “Hope, British Columbia, Canada” may be ranker higher thanthe place-entity “Hope for the future”. Although this disclosuredescribes querying and ranking particular place-entities in a particularmanner, this disclosure contemplates querying and ranking any suitableplace-entities in any suitable manner.

In particular embodiments, the social-networking system 1160 mayreceive, from a client system 1130 associated with a user, a querycomprising one or more n-grams. In particular embodiments, thesocial-networking system 1160 may identify one or more place-entitiesmatching at least a portion of the query. The identified place-entitiesmay comprise at least one place-entity corresponding to the place-entitycluster. In particular embodiments, the social-networking system 1160may send, to the client system 1130 responsive to the query,instructions for presenting a search-results page. The search-resultspage may comprise one or more search results corresponding to the one ormore identified place-entities, respectively. Each search result maycorrespond to an identified place-entity that does not correspond to theplace-entity cluster. As an example and not by way of limitation, a usermay submit a query comprising the n-gram “hope”. A place-entity “Hopefor the future” and a place-entity “Hope, British Columbia, Canada” maybe identified. The place-entity “Hope for the future” may correspond tothe place-entities cluster and may be identified as corresponding to aninvalid geographic location. The place-entity “Hope, British Columbia,Canada” may not correspond to the place-entities cluster and may becorrespond to a valid geographic location (e.g., a municipality). Asearch result may correspond to the place-entity “Hope, BritishColumbia, Canada” and there may be no search result corresponding to theplace-entity “Hope for the future”. Although this disclosure describesquerying particular place-entities in a particular manner, thisdisclosure contemplates querying any suitable place-entities in anysuitable manner.

FIG. 10 illustrates an example method 1000 for identifyingplace-entities corresponding to an invalid geographic location. Themethod may begin at step 1010, where the social-networking system 1160may access a place-entities graph comprising a plurality of place-entitynodes, each place-entity node representing a place-entity correspondingto a particular geographic location, each edge between two nodesestablishing a single degree of separation between them. At step 1020,the social-networking system 1160 may identify a place-entity clusterwithin the place-entities graph, wherein the place-entity clustercomprises a plurality of place-entity nodes corresponding to a pluralityof place-entities, respectively, each place-entity corresponding to thesame geographic location. At step 1030, the social-networking system1160 may access a plurality of embeddings representing the plurality ofplace-entities corresponding to the place-entity cluster, respectively,wherein each embedding is a point in a d-dimensional embedding space. Atstep 1040, the social-networking system 1160 may calculate, using amachine-learning model, a cluster-quality score of the place-entitycluster based on the plurality of embeddings representing theplace-entities corresponding to the place-entity cluster, wherein thecluster-quality score represents a probability that the place-entitiescorresponding to the place-entity cluster correspond to a validgeographic location. At step 1050, the social-networking system 1160 mayidentify the place-entities corresponding to the place-entity cluster ascorresponding to an invalid geographic location based on a determiningthat the cluster-quality score is less than a threshold cluster-qualityscore. Particular embodiments may repeat one or more steps of the methodof FIG. 10 , where appropriate. Although this disclosure describes andillustrates particular steps of the method of FIG. 10 as occurring in aparticular order, this disclosure contemplates any suitable steps of themethod of FIG. 10 occurring in any suitable order. Moreover, althoughthis disclosure describes and illustrates an example method foridentifying place-entities corresponding to an invalid geographiclocation including the particular steps of the method of FIG. 10 , thisdisclosure contemplates any suitable method for identifyingplace-entities corresponding to an invalid geographic location includingany suitable steps, which may include all, some, or none of the steps ofthe method of FIG. 10 , where appropriate. Furthermore, although thisdisclosure describes and illustrates particular components, devices, orsystems carrying out particular steps of the method of FIG. 10 , thisdisclosure contemplates any suitable combination of any suitablecomponents, devices, or systems carrying out any suitable steps of themethod of FIG. 10 .

System Overview

FIG. 11 illustrates an example network environment 1100 associated witha social-networking system. Network environment 1100 includes a clientsystem 1130, a social-networking system 1160, and a third-party system1170 connected to each other by a network 1110. Although FIG. 11illustrates a particular arrangement of client system 1130,social-networking system 1160, third-party system 1170, and network1110, this disclosure contemplates any suitable arrangement of clientsystem 1130, social-networking system 1160, third-party system 1170, andnetwork 1110. As an example and not by way of limitation, two or more ofclient system 1130, social-networking system 1160, and third-partysystem 1170 may be connected to each other directly, bypassing network1110. As another example, two or more of client system 1130,social-networking system 1160, and third-party system 1170 may bephysically or logically co-located with each other in whole or in part.Moreover, although FIG. 11 illustrates a particular number of clientsystems 1130, social-networking systems 1160, third-party systems 1170,and networks 1110, this disclosure contemplates any suitable number ofclient systems 1130, social-networking systems 1160, third-party systems1170, and networks 1110. As an example and not by way of limitation,network environment 1100 may include multiple client system 1130,social-networking systems 1160, third-party systems 1170, and networks1110.

This disclosure contemplates any suitable network 1110. As an exampleand not by way of limitation, one or more portions of network 1110 mayinclude an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), a portion of the Internet, a portion of the Public SwitchedTelephone Network (PSTN), a cellular telephone network, or a combinationof two or more of these. Network 1110 may include one or more networks1110.

Links 1150 may connect client system 1130, social-networking system1160, and third-party system 1170 to communication network 1110 or toeach other. This disclosure contemplates any suitable links 1150. Inparticular embodiments, one or more links 1150 include one or morewireline (such as for example Digital Subscriber Line (DSL) or Data OverCable Service Interface Specification (DOCSIS)), wireless (such as forexample Wi-Fi or Worldwide Interoperability for Microwave Access(WiMAX)), or optical (such as for example Synchronous Optical Network(SONET) or Synchronous Digital Hierarchy (SDH)) links. In particularembodiments, one or more links 1150 each include an ad hoc network, anintranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, aportion of the Internet, a portion of the PSTN, a cellulartechnology-based network, a satellite communications technology-basednetwork, another link 1150, or a combination of two or more such links1150. Links 1150 need not necessarily be the same throughout networkenvironment 1100. One or more first links 1150 may differ in one or morerespects from one or more second links 1150.

In particular embodiments, client system 1130 may be an electronicdevice including hardware, software, or embedded logic components or acombination of two or more such components and capable of carrying outthe appropriate functionalities implemented or supported by clientsystem 1130. As an example and not by way of limitation, a client system1130 may include a computer system such as a desktop computer, notebookor laptop computer, netbook, a tablet computer, e-book reader, GPSdevice, camera, personal digital assistant (PDA), handheld electronicdevice, cellular telephone, smartphone, augmented/virtual realitydevice, other suitable electronic device, or any suitable combinationthereof. This disclosure contemplates any suitable client systems 1130.A client system 1130 may enable a network user at client system 1130 toaccess network 1110. A client system 1130 may enable its user tocommunicate with other users at other client systems 1130.

In particular embodiments, client system 1130 may include a web browser1132, such as MICROSOFT INTERNET EXPLORER, GOOGLE CHROME or MOZILLAFIREFOX, and may have one or more add-ons, plug-ins, or otherextensions, such as TOOLBAR or YAHOO TOOLBAR. A user at client system1130 may enter a Uniform Resource Locator (URL) or other addressdirecting the web browser 1132 to a particular server (such as server1162, or a server associated with a third-party system 1170), and theweb browser 1132 may generate a Hyper Text Transfer Protocol (HTTP)request and communicate the HTTP request to server. The server mayaccept the HTTP request and communicate to client system 1130 one ormore Hyper Text Markup Language (HTML) files responsive to the HTTPrequest. Client system 1130 may render a webpage based on the HTML filesfrom the server for presentation to the user. This disclosurecontemplates any suitable webpage files. As an example and not by way oflimitation, webpages may render from HTML files, Extensible Hyper TextMarkup Language (XHTML) files, or Extensible Markup Language (XML)files, according to particular needs. Such pages may also executescripts such as, for example and without limitation, those written inJAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT, combinations of markup languageand scripts such as AJAX (Asynchronous JAVASCRIPT and XML), and thelike. Herein, reference to a webpage encompasses one or morecorresponding webpage files (which a browser may use to render thewebpage) and vice versa, where appropriate.

In particular embodiments, social-networking system 1160 may be anetwork-addressable computing system that can host an online socialnetwork. Social-networking system 1160 may generate, store, receive, andsend social-networking data, such as, for example, user-profile data,concept-profile data, social-graph information, or other suitable datarelated to the online social network. Social-networking system 1160 maybe accessed by the other components of network environment 1100 eitherdirectly or via network 1110. As an example and not by way oflimitation, client system 1130 may access social-networking system 1160using a web browser 1132, or a native application associated withsocial-networking system 1160 (e.g., a mobile social-networkingapplication, a messaging application, another suitable application, orany combination thereof) either directly or via network 1110. Inparticular embodiments, social-networking system 1160 may include one ormore servers 1162. Each server 1162 may be a unitary server or adistributed server spanning multiple computers or multiple datacenters.Servers 1162 may be of various types, such as, for example and withoutlimitation, web server, news server, mail server, message server,advertising server, file server, application server, exchange server,database server, proxy server, another server suitable for performingfunctions or processes described herein, or any combination thereof. Inparticular embodiments, each server 1162 may include hardware, software,or embedded logic components or a combination of two or more suchcomponents for carrying out the appropriate functionalities implementedor supported by server 1162. In particular embodiments,social-networking system 1160 may include one or more data stores 1164.Data stores 1164 may be used to store various types of information. Inparticular embodiments, the information stored in data stores 1164 maybe organized according to specific data structures. In particularembodiments, each data store 1164 may be a relational, columnar,correlation, or other suitable database. Although this disclosuredescribes or illustrates particular types of databases, this disclosurecontemplates any suitable types of databases. Particular embodiments mayprovide interfaces that enable a client system 1130, a social-networkingsystem 1160, or a third-party system 1170 to manage, retrieve, modify,add, or delete, the information stored in data store 1164.

In particular embodiments, social-networking system 1160 may store oneor more social graphs in one or more data stores 1164. In particularembodiments, a social graph may include multiple nodes—which may includemultiple user nodes (each corresponding to a particular user) ormultiple concept nodes (each corresponding to a particular concept)—andmultiple edges connecting the nodes. Social-networking system 1160 mayprovide users of the online social network the ability to communicateand interact with other users. In particular embodiments, users may jointhe online social network via social-networking system 1160 and then addconnections (e.g., relationships) to a number of other users ofsocial-networking system 1160 to whom they want to be connected. Herein,the term “friend” may refer to any other user of social-networkingsystem 1160 with whom a user has formed a connection, association, orrelationship via social-networking system 1160.

In particular embodiments, social-networking system 1160 may provideusers with the ability to take actions on various types of items orobjects, supported by social-networking system 1160. As an example andnot by way of limitation, the items and objects may include groups orsocial networks to which users of social-networking system 1160 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use, transactions that allowusers to buy or sell items via the service, interactions withadvertisements that a user may perform, or other suitable items orobjects. A user may interact with anything that is capable of beingrepresented in social-networking system 1160 or by an external system ofthird-party system 1170, which is separate from social-networking system1160 and coupled to social-networking system 1160 via a network 1110.

In particular embodiments, social-networking system 1160 may be capableof linking a variety of entities. As an example and not by way oflimitation, social-networking system 1160 may enable users to interactwith each other as well as receive content from third-party systems 1170or other entities, or to allow users to interact with these entitiesthrough an application programming interfaces (API) or othercommunication channels.

In particular embodiments, a third-party system 1170 may include one ormore types of servers, one or more data stores, one or more interfaces,including but not limited to APIs, one or more web services, one or morecontent sources, one or more networks, or any other suitable components,e.g., that servers may communicate with. A third-party system 1170 maybe operated by a different entity from an entity operatingsocial-networking system 1160. In particular embodiments, however,social-networking system 1160 and third-party systems 1170 may operatein conjunction with each other to provide social-networking services tousers of social-networking system 1160 or third-party systems 1170. Inthis sense, social-networking system 1160 may provide a platform, orbackbone, which other systems, such as third-party systems 1170, may useto provide social-networking services and functionality to users acrossthe Internet.

In particular embodiments, a third-party system 1170 may include athird-party content object provider. A third-party content objectprovider may include one or more sources of content objects, which maybe communicated to a client system 1130. As an example and not by way oflimitation, content objects may include information regarding things oractivities of interest to the user, such as, for example, movie showtimes, movie reviews, restaurant reviews, restaurant menus, productinformation and reviews, or other suitable information. As anotherexample and not by way of limitation, content objects may includeincentive content objects, such as coupons, discount tickets, giftcertificates, or other suitable incentive objects.

In particular embodiments, social-networking system 1160 also includesuser-generated content objects, which may enhance a user's interactionswith social-networking system 1160. User-generated content may includeanything a user can add, upload, send, or “post” to social-networkingsystem 1160. As an example and not by way of limitation, a usercommunicates posts to social-networking system 1160 from a client system1130. Posts may include data such as status updates or other textualdata, location information, photos, videos, links, music or othersimilar data or media. Content may also be added to social-networkingsystem 1160 by a third-party through a “communication channel,” such asa newsfeed or stream.

In particular embodiments, social-networking system 1160 may include avariety of servers, sub-systems, programs, modules, logs, and datastores. In particular embodiments, social-networking system 1160 mayinclude one or more of the following: a web server, action logger,API-request server, relevance-and-ranking engine, content-objectclassifier, notification controller, action log,third-party-content-object-exposure log, inference module,authorization/privacy server, search module, advertisement-targetingmodule, user-interface module, user-profile store, connection store,third-party content store, or location store. Social-networking system1160 may also include suitable components such as network interfaces,security mechanisms, load balancers, failover servers,management-and-network-operations consoles, other suitable components,or any suitable combination thereof. In particular embodiments,social-networking system 1160 may include one or more user-profilestores for storing user profiles. A user profile may include, forexample, biographic information, demographic information, behavioralinformation, social information, or other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, interests, affinities, or location. Interest informationmay include interests related to one or more categories. Categories maybe general or specific. As an example and not by way of limitation, if auser “likes” an article about a brand of shoes the category may be thebrand, or the general category of “shoes” or “clothing.” A connectionstore may be used for storing connection information about users. Theconnection information may indicate users who have similar or commonwork experience, group memberships, hobbies, educational history, or arein any way related or share common attributes. The connectioninformation may also include user-defined connections between differentusers and content (both internal and external). A web server may be usedfor linking social-networking system 1160 to one or more client systems1130 or one or more third-party system 1170 via network 1110. The webserver may include a mail server or other messaging functionality forreceiving and routing messages between social-networking system 1160 andone or more client systems 1130. An API-request server may allow athird-party system 1170 to access information from social-networkingsystem 1160 by calling one or more APIs. An action logger may be used toreceive communications from a web server about a user's actions on oroff social-networking system 1160. In conjunction with the action log, athird-party-content-object log may be maintained of user exposures tothird-party-content objects. A notification controller may provideinformation regarding content objects to a client system 1130.Information may be pushed to a client system 1130 as notifications, orinformation may be pulled from client system 1130 responsive to arequest received from client system 1130. Authorization servers may beused to enforce one or more privacy settings of the users ofsocial-networking system 1160. A privacy setting of a user determineshow particular information associated with a user can be shared. Theauthorization server may allow users to opt in to or opt out of havingtheir actions logged by social-networking system 1160 or shared withother systems (e.g., third-party system 1170), such as, for example, bysetting appropriate privacy settings. Third-party-content-object storesmay be used to store content objects received from third parties, suchas a third-party system 1170. Location stores may be used for storinglocation information received from client systems 1130 associated withusers. Advertisement-pricing modules may combine social information, thecurrent time, location information, or other suitable information toprovide relevant advertisements, in the form of notifications, to auser.

Social Graphs

FIG. 12 illustrates example social graph 1200. In particularembodiments, social-networking system 1160 may store one or more socialgraphs 1200 in one or more data stores. In particular embodiments,social graph 1200 may include multiple nodes—which may include multipleuser nodes 1202 or multiple concept nodes 1204—and multiple edges 1206connecting the nodes. Example social graph 1200 illustrated in FIG. 12is shown, for didactic purposes, in a two-dimensional visual maprepresentation. In particular embodiments, a social-networking system1160, client system 1130, or third-party system 1170 may access socialgraph 1200 and related social-graph information for suitableapplications. The nodes and edges of social graph 1200 may be stored asdata objects, for example, in a data store (such as a social-graphdatabase). Such a data store may include one or more searchable orqueryable indexes of nodes or edges of social graph 1200.

In particular embodiments, a user node 1202 may correspond to a user ofsocial-networking system 1160. As an example and not by way oflimitation, a user may be an individual (human user), an entity (e.g.,an enterprise, business, or third-party application), or a group (e.g.,of individuals or entities) that interacts or communicates with or oversocial-networking system 1160. In particular embodiments, when a userregisters for an account with social-networking system 1160,social-networking system 1160 may create a user node 1202 correspondingto the user, and store the user node 1202 in one or more data stores.Users and user nodes 1202 described herein may, where appropriate, referto registered users and user nodes 1202 associated with registeredusers. In addition or as an alternative, users and user nodes 1202described herein may, where appropriate, refer to users that have notregistered with social-networking system 1160. In particularembodiments, a user node 1202 may be associated with informationprovided by a user or information gathered by various systems, includingsocial-networking system 1160. As an example and not by way oflimitation, a user may provide his or her name, profile picture, contactinformation, birth date, sex, marital status, family status, employment,education background, preferences, interests, or other demographicinformation. In particular embodiments, a user node 1202 may beassociated with one or more data objects corresponding to informationassociated with a user. In particular embodiments, a user node 1202 maycorrespond to one or more webpages.

In particular embodiments, a concept node 1204 may correspond to aconcept. As an example and not by way of limitation, a concept maycorrespond to a place (such as, for example, a movie theater,restaurant, landmark, or city); a website (such as, for example, awebsite associated with social-network system 1160 or a third-partywebsite associated with a web-application server); an entity (such as,for example, a person, business, group, sports team, or celebrity); aresource (such as, for example, an audio file, video file, digitalphoto, text file, structured document, or application) which may belocated within social-networking system 1160 or on an external server,such as a web-application server; real or intellectual property (suchas, for example, a sculpture, painting, movie, game, song, idea,photograph, or written work); a game; an activity; an idea or theory; anobject in a augmented/virtual reality environment; another suitableconcept; or two or more such concepts. A concept node 1204 may beassociated with information of a concept provided by a user orinformation gathered by various systems, including social-networkingsystem 1160. As an example and not by way of limitation, information ofa concept may include a name or a title; one or more images (e.g., animage of the cover page of a book); a location (e.g., an address or ageographical location); a website (which may be associated with a URL);contact information (e.g., a phone number or an email address); othersuitable concept information; or any suitable combination of suchinformation. In particular embodiments, a concept node 1204 may beassociated with one or more data objects corresponding to informationassociated with concept node 1204. In particular embodiments, a conceptnode 1204 may correspond to one or more webpages.

In particular embodiments, a node in social graph 1200 may represent orbe represented by a webpage (which may be referred to as a “profilepage”). Profile pages may be hosted by or accessible tosocial-networking system 1160. Profile pages may also be hosted onthird-party websites associated with a third-party system 1170. As anexample and not by way of limitation, a profile page corresponding to aparticular external webpage may be the particular external webpage andthe profile page may correspond to a particular concept node 1204.Profile pages may be viewable by all or a selected subset of otherusers. As an example and not by way of limitation, a user node 1202 mayhave a corresponding user-profile page in which the corresponding usermay add content, make declarations, or otherwise express himself orherself. As another example and not by way of limitation, a concept node1204 may have a corresponding concept-profile page in which one or moreusers may add content, make declarations, or express themselves,particularly in relation to the concept corresponding to concept node1204.

In particular embodiments, a concept node 1204 may represent athird-party webpage or resource hosted by a third-party system 1170. Thethird-party webpage or resource may include, among other elements,content, a selectable or other icon, or other inter-actable object(which may be implemented, for example, in JavaScript, AJAX, or PHPcodes) representing an action or activity. As an example and not by wayof limitation, a third-party webpage may include a selectable icon suchas “like,” “check-in,” “eat,” “recommend,” or another suitable action oractivity. A user viewing the third-party webpage may perform an actionby selecting one of the icons (e.g., “check-in”), causing a clientsystem 1130 to send to social-networking system 1160 a messageindicating the user's action. In response to the message,social-networking system 1160 may create an edge (e.g., a check-in-typeedge) between a user node 1202 corresponding to the user and a conceptnode 1204 corresponding to the third-party webpage or resource and storeedge 1206 in one or more data stores.

In particular embodiments, a pair of nodes in social graph 1200 may beconnected to each other by one or more edges 1206. An edge 1206connecting a pair of nodes may represent a relationship between the pairof nodes. In particular embodiments, an edge 1206 may include orrepresent one or more data objects or attributes corresponding to therelationship between a pair of nodes. As an example and not by way oflimitation, a first user may indicate that a second user is a “friend”of the first user. In response to this indication, social-networkingsystem 1160 may send a “friend request” to the second user. If thesecond user confirms the “friend request,” social-networking system 1160may create an edge 1206 connecting the first user's user node 1202 tothe second user's user node 1202 in social graph 1200 and store edge1206 as social-graph information in one or more of data stores 1164. Inthe example of FIG. 12 , social graph 1200 includes an edge 1206indicating a friend relation between user nodes 1202 of user “A” anduser “B” and an edge indicating a friend relation between user nodes1202 of user “C” and user “B.” Although this disclosure describes orillustrates particular edges 1206 with particular attributes connectingparticular user nodes 1202, this disclosure contemplates any suitableedges 1206 with any suitable attributes connecting user nodes 1202. Asan example and not by way of limitation, an edge 1206 may represent afriendship, family relationship, business or employment relationship,fan relationship (including, e.g., liking, etc.), follower relationship,visitor relationship (including, e.g., accessing, viewing, checking-in,sharing, etc.), subscriber relationship, superior/subordinaterelationship, reciprocal relationship, non-reciprocal relationship,another suitable type of relationship, or two or more suchrelationships. Moreover, although this disclosure generally describesnodes as being connected, this disclosure also describes users orconcepts as being connected. Herein, references to users or conceptsbeing connected may, where appropriate, refer to the nodes correspondingto those users or concepts being connected in social graph 1200 by oneor more edges 1206.

In particular embodiments, an edge 1206 between a user node 1202 and aconcept node 1204 may represent a particular action or activityperformed by a user associated with user node 1202 toward a conceptassociated with a concept node 1204. As an example and not by way oflimitation, as illustrated in FIG. 12 , a user may “like,” “attended,”“played,” “listened,” “cooked,” “worked at,” or “watched” a concept,each of which may correspond to an edge type or subtype. Aconcept-profile page corresponding to a concept node 1204 may include,for example, a selectable “check in” icon (such as, for example, aclickable “check in” icon) or a selectable “add to favorites” icon.Similarly, after a user clicks these icons, social-networking system1160 may create a “favorite” edge or a “check in” edge in response to auser's action corresponding to a respective action. As another exampleand not by way of limitation, a user (user “C”) may listen to aparticular song (“Imagine”) using a particular application (SPOTIFY,which is an online music application). In this case, social-networkingsystem 1160 may create a “listened” edge 1206 and a “used” edge (asillustrated in FIG. 12 ) between user nodes 1202 corresponding to theuser and concept nodes 1204 corresponding to the song and application toindicate that the user listened to the song and used the application.Moreover, social-networking system 1160 may create a “played” edge 1206(as illustrated in FIG. 12 ) between concept nodes 1204 corresponding tothe song and the application to indicate that the particular song wasplayed by the particular application. In this case, “played” edge 1206corresponds to an action performed by an external application (SPOTIFY)on an external audio file (the song “Imagine”). Although this disclosuredescribes particular edges 1206 with particular attributes connectinguser nodes 1202 and concept nodes 1204, this disclosure contemplates anysuitable edges 1206 with any suitable attributes connecting user nodes1202 and concept nodes 1204. Moreover, although this disclosuredescribes edges between a user node 1202 and a concept node 1204representing a single relationship, this disclosure contemplates edgesbetween a user node 1202 and a concept node 1204 representing one ormore relationships. As an example and not by way of limitation, an edge1206 may represent both that a user likes and has used at a particularconcept. Alternatively, another edge 1206 may represent each type ofrelationship (or multiples of a single relationship) between a user node1202 and a concept node 1204 (as illustrated in FIG. 12 between usernode 1202 for user “E” and concept node 1204 for “SPOTIFY”).

In particular embodiments, social-networking system 1160 may create anedge 1206 between a user node 1202 and a concept node 1204 in socialgraph 1200. As an example and not by way of limitation, a user viewing aconcept-profile page (such as, for example, by using a web browser or aspecial-purpose application hosted by the user's client system 1130) mayindicate that he or she likes the concept represented by the conceptnode 1204 by clicking or selecting a “Like” icon, which may cause theuser's client system 1130 to send to social-networking system 1160 amessage indicating the user's liking of the concept associated with theconcept-profile page. In response to the message, social-networkingsystem 1160 may create an edge 1206 between user node 1202 associatedwith the user and concept node 1204, as illustrated by “like” edge 1206between the user and concept node 1204. In particular embodiments,social-networking system 1160 may store an edge 1206 in one or more datastores. In particular embodiments, an edge 1206 may be automaticallyformed by social-networking system 1160 in response to a particular useraction. As an example and not by way of limitation, if a first useruploads a picture, watches a movie, or listens to a song, an edge 1206may be formed between user node 1202 corresponding to the first user andconcept nodes 1204 corresponding to those concepts. Although thisdisclosure describes forming particular edges 1206 in particularmanners, this disclosure contemplates forming any suitable edges 1206 inany suitable manner.

Privacy

In particular embodiments, one or more of the content objects of theonline social network may be associated with a privacy setting. Theprivacy settings (or “access settings”) for an object may be stored inany suitable manner, such as, for example, in association with theobject, in an index on an authorization server, in another suitablemanner, or any combination thereof. A privacy setting of an object mayspecify how the object (or particular information associated with anobject) can be accessed (e.g., viewed or shared) using the online socialnetwork. Where the privacy settings for an object allow a particularuser to access that object, the object may be described as being“visible” with respect to that user. As an example and not by way oflimitation, a user of the online social network may specify privacysettings for a user-profile page that identify a set of users that mayaccess the work experience information on the user-profile page, thusexcluding other users from accessing the information. In particularembodiments, the privacy settings may specify a “blocked list” of usersthat should not be allowed to access certain information associated withthe object. In other words, the blocked list may specify one or moreusers or entities for which an object is not visible. As an example andnot by way of limitation, a user may specify a set of users that may notaccess photos albums associated with the user, thus excluding thoseusers from accessing the photo albums (while also possibly allowingcertain users not within the set of users to access the photo albums).In particular embodiments, privacy settings may be associated withparticular social-graph elements. Privacy settings of a social-graphelement, such as a node or an edge, may specify how the social-graphelement, information associated with the social-graph element, orcontent objects associated with the social-graph element can be accessedusing the online social network. As an example and not by way oflimitation, a particular concept node 1204 corresponding to a particularphoto may have a privacy setting specifying that the photo may only beaccessed by users tagged in the photo and their friends. In particularembodiments, privacy settings may allow users to opt in or opt out ofhaving their actions logged by social-networking system 1160 or sharedwith other systems (e.g., third-party system 1170). In particularembodiments, the privacy settings associated with an object may specifyany suitable granularity of permitted access or denial of access. As anexample and not by way of limitation, access or denial of access may bespecified for particular users (e.g., only me, my roommates, and myboss), users within a particular degrees-of-separation (e.g., friends,or friends-of-friends), user groups (e.g., the gaming club, my family),user networks (e.g., employees of particular employers, students oralumni of particular university), all users (“public”), no users(“private”), users of third-party systems 1170, particular applications(e.g., third-party applications, external websites), other suitableusers or entities, or any combination thereof. Although this disclosuredescribes using particular privacy settings in a particular manner, thisdisclosure contemplates using any suitable privacy settings in anysuitable manner.

In particular embodiments, one or more servers 1162 may beauthorization/privacy servers for enforcing privacy settings. Inresponse to a request from a user (or other entity) for a particularobject stored in a data store 1164, social-networking system 1160 maysend a request to the data store 1164 for the object. The request mayidentify the user associated with the request and may only be sent tothe user (or a client system 1130 of the user) if the authorizationserver determines that the user is authorized to access the object basedon the privacy settings associated with the object. If the requestinguser is not authorized to access the object, the authorization servermay prevent the requested object from being retrieved from the datastore 1164, or may prevent the requested object from being sent to theuser. In the search query context, an object may only be generated as asearch result if the querying user is authorized to access the object.In other words, the object must have a visibility that is visible to thequerying user. If the object has a visibility that is not visible to theuser, the object may be excluded from the search results. Although thisdisclosure describes enforcing privacy settings in a particular manner,this disclosure contemplates enforcing privacy settings in any suitablemanner.

Systems and Methods

FIG. 13 illustrates an example computer system 1300. In particularembodiments, one or more computer systems 1300 perform one or more stepsof one or more methods described or illustrated herein. In particularembodiments, one or more computer systems 1300 provide functionalitydescribed or illustrated herein. In particular embodiments, softwarerunning on one or more computer systems 1300 performs one or more stepsof one or more methods described or illustrated herein or providesfunctionality described or illustrated herein. Particular embodimentsinclude one or more portions of one or more computer systems 1300.Herein, reference to a computer system may encompass a computing device,and vice versa, where appropriate. Moreover, reference to a computersystem may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems1300. This disclosure contemplates computer system 1300 taking anysuitable physical form. As example and not by way of limitation,computer system 1300 may be an embedded computer system, asystem-on-chip (SOC), a single-board computer system (SBC) (such as, forexample, a computer-on-module (COM) or system-on-module (SOM)), adesktop computer system, a laptop or notebook computer system, aninteractive kiosk, a mainframe, a mesh of computer systems, a mobiletelephone, a personal digital assistant (PDA), a server, a tabletcomputer system, an augmented/virtual reality device, or a combinationof two or more of these. Where appropriate, computer system 1300 mayinclude one or more computer systems 1300; be unitary or distributed;span multiple locations; span multiple machines; span multiple datacenters; or reside in a cloud, which may include one or more cloudcomponents in one or more networks. Where appropriate, one or morecomputer systems 1300 may perform without substantial spatial ortemporal limitation one or more steps of one or more methods describedor illustrated herein. As an example and not by way of limitation, oneor more computer systems 1300 may perform in real time or in batch modeone or more steps of one or more methods described or illustratedherein. One or more computer systems 1300 may perform at different timesor at different locations one or more steps of one or more methodsdescribed or illustrated herein, where appropriate.

In particular embodiments, computer system 1300 includes a processor1302, memory 1304, storage 1306, an input/output (I/O) interface 1308, acommunication interface 1310, and a bus 1312. Although this disclosuredescribes and illustrates a particular computer system having aparticular number of particular components in a particular arrangement,this disclosure contemplates any suitable computer system having anysuitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 1302 includes hardware forexecuting instructions, such as those making up a computer program. Asan example and not by way of limitation, to execute instructions,processor 1302 may retrieve (or fetch) the instructions from an internalregister, an internal cache, memory 1304, or storage 1306; decode andexecute them; and then write one or more results to an internalregister, an internal cache, memory 1304, or storage 1306. In particularembodiments, processor 1302 may include one or more internal caches fordata, instructions, or addresses. This disclosure contemplates processor1302 including any suitable number of any suitable internal caches,where appropriate. As an example and not by way of limitation, processor1302 may include one or more instruction caches, one or more datacaches, and one or more translation lookaside buffers (TLBs).Instructions in the instruction caches may be copies of instructions inmemory 1304 or storage 1306, and the instruction caches may speed upretrieval of those instructions by processor 1302. Data in the datacaches may be copies of data in memory 1304 or storage 1306 forinstructions executing at processor 1302 to operate on; the results ofprevious instructions executed at processor 1302 for access bysubsequent instructions executing at processor 1302 or for writing tomemory 1304 or storage 1306; or other suitable data. The data caches mayspeed up read or write operations by processor 1302. The TLBs may speedup virtual-address translation for processor 1302. In particularembodiments, processor 1302 may include one or more internal registersfor data, instructions, or addresses. This disclosure contemplatesprocessor 1302 including any suitable number of any suitable internalregisters, where appropriate. Where appropriate, processor 1302 mayinclude one or more arithmetic logic units (ALUs); be a multi-coreprocessor; or include one or more processors 1302. Although thisdisclosure describes and illustrates a particular processor, thisdisclosure contemplates any suitable processor.

In particular embodiments, memory 1304 includes main memory for storinginstructions for processor 1302 to execute or data for processor 1302 tooperate on. As an example and not by way of limitation, computer system1300 may load instructions from storage 1306 or another source (such as,for example, another computer system 1300) to memory 1304. Processor1302 may then load the instructions from memory 1304 to an internalregister or internal cache. To execute the instructions, processor 1302may retrieve the instructions from the internal register or internalcache and decode them. During or after execution of the instructions,processor 1302 may write one or more results (which may be intermediateor final results) to the internal register or internal cache. Processor1302 may then write one or more of those results to memory 1304. Inparticular embodiments, processor 1302 executes only instructions in oneor more internal registers or internal caches or in memory 1304 (asopposed to storage 1306 or elsewhere) and operates only on data in oneor more internal registers or internal caches or in memory 1304 (asopposed to storage 1306 or elsewhere). One or more memory buses (whichmay each include an address bus and a data bus) may couple processor1302 to memory 1304. Bus 1312 may include one or more memory buses, asdescribed below. In particular embodiments, one or more memorymanagement units (MMUs) reside between processor 1302 and memory 1304and facilitate accesses to memory 1304 requested by processor 1302. Inparticular embodiments, memory 1304 includes random access memory (RAM).This RAM may be volatile memory, where appropriate. Where appropriate,this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, whereappropriate, this RAM may be single-ported or multi-ported RAM. Thisdisclosure contemplates any suitable RAM. Memory 1304 may include one ormore memories 1304, where appropriate. Although this disclosuredescribes and illustrates particular memory, this disclosurecontemplates any suitable memory.

In particular embodiments, storage 1306 includes mass storage for dataor instructions. As an example and not by way of limitation, storage1306 may include a hard disk drive (HDD), a floppy disk drive, flashmemory, an optical disc, a magneto-optical disc, magnetic tape, or aUniversal Serial Bus (USB) drive or a combination of two or more ofthese. Storage 1306 may include removable or non-removable (or fixed)media, where appropriate. Storage 1306 may be internal or external tocomputer system 1300, where appropriate. In particular embodiments,storage 1306 is non-volatile, solid-state memory. In particularembodiments, storage 1306 includes read-only memory (ROM). Whereappropriate, this ROM may be mask-programmed ROM, programmable ROM(PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM),electrically alterable ROM (EAROM), or flash memory or a combination oftwo or more of these. This disclosure contemplates mass storage 1306taking any suitable physical form. Storage 1306 may include one or morestorage control units facilitating communication between processor 1302and storage 1306, where appropriate. Where appropriate, storage 1306 mayinclude one or more storages 1306. Although this disclosure describesand illustrates particular storage, this disclosure contemplates anysuitable storage.

In particular embodiments, I/O interface 1308 includes hardware,software, or both, providing one or more interfaces for communicationbetween computer system 1300 and one or more I/O devices. Computersystem 1300 may include one or more of these I/O devices, whereappropriate. One or more of these I/O devices may enable communicationbetween a person and computer system 1300. As an example and not by wayof limitation, an I/O device may include a keyboard, keypad, microphone,monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet,touch screen, trackball, video camera, another suitable I/O device or acombination of two or more of these. An I/O device may include one ormore sensors. This disclosure contemplates any suitable I/O devices andany suitable I/O interfaces 1308 for them. Where appropriate, I/Ointerface 1308 may include one or more device or software driversenabling processor 1302 to drive one or more of these I/O devices. I/Ointerface 1308 may include one or more I/O interfaces 1308, whereappropriate. Although this disclosure describes and illustrates aparticular I/O interface, this disclosure contemplates any suitable I/Ointerface.

In particular embodiments, communication interface 1310 includeshardware, software, or both providing one or more interfaces forcommunication (such as, for example, packet-based communication) betweencomputer system 1300 and one or more other computer systems 1300 or oneor more networks. As an example and not by way of limitation,communication interface 1310 may include a network interface controller(NIC) or network adapter for communicating with an Ethernet or otherwire-based network or a wireless NIC (WNIC) or wireless adapter forcommunicating with a wireless network, such as a WI-FI network. Thisdisclosure contemplates any suitable network and any suitablecommunication interface 1310 for it. As an example and not by way oflimitation, computer system 1300 may communicate with an ad hoc network,a personal area network (PAN), a local area network (LAN), a wide areanetwork (WAN), a metropolitan area network (MAN), or one or moreportions of the Internet or a combination of two or more of these. Oneor more portions of one or more of these networks may be wired orwireless. As an example, computer system 1300 may communicate with awireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FInetwork, a WI-MAX network, a cellular telephone network (such as, forexample, a Global System for Mobile Communications (GSM) network), orother suitable wireless network or a combination of two or more ofthese. Computer system 1300 may include any suitable communicationinterface 1310 for any of these networks, where appropriate.Communication interface 1310 may include one or more communicationinterfaces 1310, where appropriate. Although this disclosure describesand illustrates a particular communication interface, this disclosurecontemplates any suitable communication interface.

In particular embodiments, bus 1312 includes hardware, software, or bothcoupling components of computer system 1300 to each other. As an exampleand not by way of limitation, bus 1312 may include an AcceleratedGraphics Port (AGP) or other graphics bus, an Enhanced Industry StandardArchitecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT)interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBANDinterconnect, a low-pin-count (LPC) bus, a memory bus, a Micro ChannelArchitecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, aPCI-Express (PCIe) bus, a serial advanced technology attachment (SATA)bus, a Video Electronics Standards Association local (VLB) bus, oranother suitable bus or a combination of two or more of these. Bus 1312may include one or more buses 1312, where appropriate. Although thisdisclosure describes and illustrates a particular bus, this disclosurecontemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such, as for example, field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Miscellaneous

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method comprising, by one or more computersystems of an online social network: receiving, from a client systemassociated with a first user of the online social network, dataindicating that the first user is located at a first geographic locationat a first time; accessing a first embedding representing a firstplace-entity corresponding to the first geographic location, wherein thefirst embedding is a d-dimensional point in a d-dimensional embeddingspace, wherein the first embedding was generated through a training of amachine-learning model, based on a plurality of features of a pluralityof first feature-types, respectively; identifying a plurality of secondplace-entities, each second place-entity corresponding to a secondgeographic location visited by a second user who shares commonconnection information with the first user on the online social network,wherein each of the second geographic locations is within apredetermined distance from the first geographic location; accessing aplurality of second embeddings representing the plurality of secondplace-entities, respectively, wherein each second embedding is ad-dimensional point in the d-dimensional embedding space, wherein eachof the plurality of second embeddings was generated through the trainingof the machine-learning model, based on a plurality of features of aplurality of second feature-types, respectively, wherein the pluralityof second feature types comprise (1) a time of the visit of therespective second geographic location by the second user who sharescommon connection information with the first user on the online socialnetwork, and (2) an hours of operation of the respective secondplace-entity; calculating, for each of the second place-entities, acosine similarity metric between first vector representation of thed-dimensional point of the embedding representing the first place-entityand a second vector representation of the d-dimensional point of theembedding representing the second place-entity, wherein the cosinesimilarity metric corresponds to a probability that the first user willbe located at the second geographic location corresponding to the secondplace entity within a particular timeframe of the first user beinglocated at the first geographic location at the first time; ranking eachof the second place-entities based on their cosine similarity metrics;and sending, to the client system, instructions for presenting asuggestion for visiting one or more of the second geographic locationscorresponding to one or more of the second place-entities having aranking greater than a threshold ranking.
 2. The method of claim 1,wherein at least one second place-entity corresponds to a secondgeographic location previously visited by the first user.
 3. The methodof claim 1, further comprising generating, through the training of themachine-learning model, the first embedding and each of the secondembeddings.
 4. The method of claim 1, wherein the machine-learning modelwas trained by: accessing a plurality of positive training pairs oftraining place-entities, wherein each positive training pair comprises afirst training place-entity corresponding to a first training geographiclocation that a second user was located at and a second trainingplace-entity corresponding to a second training geographic location thatthe second user was located at within the particular timeframe of thesecond user being located at the first training geographic location;accessing a plurality of negative training pairs of trainingplace-entities, wherein each negative training pair comprises a firsttraining place-entity corresponding to a first training geographiclocation that a second user was located at and a second trainingplace-entity corresponding to a second training geographic location thatthe second user was not located within the particular timeframe of thesecond user being located at the first training geographic location; andtraining the machine-learning model to generate an embeddingrepresenting a first place-entity and an embedding representing a secondplace-entity by optimizing an objective function based on the pluralityof positive training pairs and the plurality of negative training pairs.5. The method of claim 4, wherein, for each negative training pair, thesecond training geographic location is within a predetermined geographicdistance of the first training geographic location.
 6. The method ofclaim 4, further comprising: accessing a plurality of evaluation pairsof evaluation place-entities, wherein each evaluation pair comprises afirst evaluation place-entity corresponding to a first evaluationgeographic location that a second user was located at and a secondevaluation place-entity corresponding to a second evaluation geographiclocation that the second user was located at within the particulartimeframe of the second user being located at the first evaluationgeographic location; generating, for each first evaluation place-entity,an ordered list of predicted second place entities; and calculating amean-reciprocal rank associated with the machine-learning model, whereinthe mean-reciprocal rank is based on a plurality of reciprocal rankscorresponding to the plurality of evaluation pairs, wherein eachreciprocal rank corresponding to an evaluation pair was calculated basedon the first evaluation place-entity of the evaluation pair, the secondevaluation place-entity of the evaluation pair, and the ordered list ofpredicted second place entities corresponding to the first evaluationplace-entity of the evaluation pair.
 7. The method of claim 1, whereinthe features of a place-entity comprise a category of the place-entity,the first time, the hours of operation of a business located at ageographic location corresponding to the place-entity, popular hours ofa business located at geographic location corresponding to theplace-entity, or any combination thereof.
 8. The method of claim 1,wherein the feature-types of the features used to generate the firstembedding of the first place-entity are not identical to feature-typesof the features used to generate the second embeddings of the secondplace-entities.
 9. One or more computer-readable non-transitory storagemedia embodying software that is operable when executed to: receive,from a client system associated with a first user of the online socialnetwork, data indicating that the first user is located at a firstgeographic location at a first time; access a first embeddingrepresenting a first place-entity corresponding to the first geographiclocation, wherein the first embedding is a d-dimensional point in ad-dimensional embedding space, wherein the first embedding was generatedthrough a training of a machine-learning model, based on a plurality offeatures of a plurality of first feature-types, respectively; identify aplurality of second place-entities, each second place-entitycorresponding to a second geographic location visited by a second userwho shares common connection information with the first user on theonline social network, wherein each of the second geographic locationsis within a predetermined distance from the first geographic location;access a plurality of second embeddings representing the plurality ofsecond place-entities, respectively, wherein each second embedding is ad-dimensional point in the d-dimensional embedding space, wherein eachof the plurality of second embeddings was generated through the trainingof the machine-learning model, based on a plurality of features of aplurality of second feature-types, respectively, wherein the pluralityof second feature types comprise (1) a time of the visit of therespective second geographic location by the second user who sharescommon connection information with the first user on the online socialnetwork, and (2) an hours of operation of the respective secondplace-entity; calculate, for each of the second place-entities, a cosinesimilarity metric between a first vector representation of thed-dimensional point of the embedding representing the first place-entityand a second vector representation of the d-dimensional point of theembedding representing the second place-entity, wherein the cosinesimilarity metric corresponds to a probability that the first user willbe located at the second geographic location corresponding to the secondplace entity within a particular timeframe of the first user beinglocated at the first geographic location at the first time; rank each ofthe second place-entities based on their cosine calculated similaritymetrics; and sending, to the client system, instructions for presentinga suggestion for visiting one or more of the second geographic locationscorresponding to one or more of the second place-entities having aranking greater than a threshold ranking.
 10. The media of claim 9,wherein at least one second place-entity corresponds to a secondgeographic location previously visited by the first user.
 11. The mediaof claim 9, wherein the software is further operable when executed togenerate, through the training of the machine-learning model, the firstembedding and each of the second embeddings.
 12. The media of claim 9,wherein the machine-learning model was trained by: accessing a pluralityof positive training pairs of training place-entities, wherein eachpositive training pair comprises a first training place-entitycorresponding to a first training geographic location that a second userwas located at and a second training place-entity corresponding to asecond training geographic location that the second user was located atwithin the particular timeframe of the second user being located at thefirst training geographic location; accessing a plurality of negativetraining pairs of training place-entities, wherein each negativetraining pair comprises a first training place-entity corresponding to afirst training geographic location that a second user was located at anda second training place-entity corresponding to a second traininggeographic location that the second user was not located within theparticular timeframe of the second user being located at the firsttraining geographic location; and training the machine-learning model togenerate an embedding representing a first place-entity and an embeddingrepresenting a second place-entity by optimizing an objective functionbased on the plurality of positive training pairs and the plurality ofnegative training pairs.
 13. A system comprising: one or moreprocessors; and a non-transitory memory coupled to the processorscomprising instructions executable by the processors, the processorsoperable when executing the instructions to: receive, from a clientsystem associated with a first user of the online social network, dataindicating that the first user is located at a first geographic locationat a first time; access a first embedding representing a firstplace-entity corresponding to the first geographic location, wherein thefirst embedding is a d-dimensional point in a d-dimensional embeddingspace, wherein the first embedding was generated through a training of amachine-learning model, based on a plurality of features of a pluralityof first feature-types, respectively; identify a plurality of secondplace-entities, each second place-entity corresponding to a secondgeographic location visited by a second user who shares commonconnection information with the first user on the online social network,wherein each of the second geographic locations is within apredetermined distance from the first geographic location; access aplurality of second embeddings representing the plurality of secondplace-entities, respectively, wherein each second embedding is ad-dimensional point in the d-dimensional embedding space, wherein eachof the plurality of second embeddings was generated through the trainingof the machine-learning model, based on a plurality of features of aplurality of second feature-types, respectively, wherein the pluralityof second feature types comprise (1) a time of the visit of therespective second geographic location by the second user who sharescommon connection information with the first user on the online socialnetwork, and (2) an hours of operation of the respective secondplace-entity; calculate, for each of the second place-entities, a cosinesimilarity metric between a first vector representation of thed-dimensional point of the embedding representing the first place-entityand a second vector representation of the d-dimensional point of theembedding representing the second place-entity, wherein the cosinesimilarity metric corresponds to a probability that the first user willbe located at the second geographic location corresponding to the secondplace entity within a particular timeframe of the first user beinglocated at the first geographic location at the first time; rank each ofthe second place-entities based on their cosine calculated similaritymetrics; and sending, to the client system, instructions for presentinga suggestion for visiting one or more of the second geographic locationscorresponding to one or more of the second place-entities having aranking greater than a threshold ranking.